Multi-sensor determination of semiconductor equipment status

Abstract

In some embodiments disclosed herein, methods and apparatus are provided for multi-sensor determination of a state of semiconductor equipment, the semiconductor manufacturing equipment comprising a plurality of sensors including one or more spatial sensors, one or more spectral sensors, and one or more temporal sensors disposed about the semiconductor manufacturing equipment, and a controller communicatively coupled to the plurality of sensors, the controller configured to determine a set of signals to monitor during a process performed by the semiconductor manufacturing equipment from the plurality of sensors, obtain measurements associated with the set of signals from the plurality of sensors during the process, and determine an indicator of the state of the semiconductor manufacturing equipment based on a combination of data generated from the measurements associated with the set of signals.

Description

Incorporation by Reference

[0001] A PCT application is being filed concurrently herewith as part of this application, and each application identified in that concurrently filed PCT application to which this application claims benefit or priority is incorporated herein by reference in its entirety for all purposes. [Background technology]

[0002] Sensors associated with semiconductor equipment, such as multi-station fabrication tools (e.g., including multi-station process chambers), typically perform one specialized type of task. For example, automated infrared endpoint detection (IR-EPD) is used for endpoint detection during cleaning for all stations (e.g., all four stations of a multi-station process chamber). Some sensors address specific classes of problems. For example, one or more cameras are used alone to characterize the plasma. In addition, ex-situ measurements may be performed using metrology tools, each of which can provide an indication of a particular condition or property of the equipment (e.g., film thickness, resistivity, refractive index (RI), stress, etc.). Collectively, sensors or metrology tools are typically employed for a specific task or class of problem. The background and context discussion contained herein is provided solely for the purpose of generally presenting the contents of the present disclosure. Much of the present disclosure presents the inventors' work, and it is not meant that such work is admitted as prior art merely because it is discussed in the Background section or presented as context elsewhere herein. Summary of the Invention

[0003] In one aspect of the present disclosure, a manufacturing system is disclosed. In some embodiments, the manufacturing system includes semiconductor manufacturing equipment; a plurality of sensors including one or more spatial sensors, one or more spectral sensors, and one or more temporal sensors disposed about the semiconductor manufacturing equipment; and a controller communicatively coupled to the plurality of sensors. The controller is configured to determine a set of signals to monitor from the plurality of sensors during a process performed by the semiconductor manufacturing equipment, obtain measurements associated with the set of signals from the plurality of sensors during the process, and determine an indicator of a state of the semiconductor manufacturing equipment based on a combination of data generated from the measurements associated with the set of signals.

[0004] In another aspect of the present disclosure, a method is disclosed for multi-sensor determination of a state of semiconductor equipment. In some embodiments, the method comprises determining a set of signals to monitor during a process performed by the semiconductor equipment from a plurality of sensors of the semiconductor equipment, the plurality of sensors including at least one spatial sensor, at least one spectral sensor, and at least one temporal sensor, performing sensor measurements via two or more of (i) the at least one spatial sensor, (ii) the at least one spectral sensor, or (iii) the at least one temporal sensor based on the process being performed, and determining a state of the semiconductor equipment based on the sensor measurements.

[0005] In another aspect of the present disclosure, a multi-sensor measurement device is disclosed. In some embodiments, the multi-sensor measurement device includes a housing having a major dimension of about 10 inches or less, a spectral sensor within the housing, a spatial sensor within the housing, a time sensor within the housing, and a physical interface shaped to allow the housing to be attached to a surface of a fabrication tool and to allow reception, through a window in the fabrication tool, of electromagnetic signals associated with a process performed by the fabrication tool.

[0006] In another aspect of the present disclosure, a method for multi-sensor determination of process chamber cleaning endpoint is disclosed.

[0007] In another aspect of the present disclosure, a method for determining the presence of unexpected species in a process chamber is disclosed.

[0008] These and other features of the disclosed embodiments are described in detail below with reference to the associated drawings. [Brief explanation of the drawings]

[0009] [Figure 1A] FIG. 1A is a diagram illustrating a fabrication tool for depositing or etching a film on or above a substrate using a plasma process, the tool including a camera sensor.

[0010] [Figure 1B] FIG. 1B is a schematic diagram of one embodiment of a multi-station processing tool, the tool including four camera sensors.

[0011] [Figure 1C] FIG. 1C is a top view of an electronic device fabrication system having four multi-station fabrication tools, one of the tools including a camera sensor.

[0012] [Figure 2] FIG. 2 is a diagram illustrating a schematic of a process chamber with an exemplary camera oriented to capture images along a horizontal line of sight into the chamber interior.

[0013] [Figure 3] FIG. 3 is a diagram illustrating a schematic of a process chamber with an exemplary camera oriented to capture images along a vertical line of sight into the chamber interior.

[0014] [Figure 4] FIG. 4 is a schematic diagram of an exemplary optical emission spectroscopy (OES) sensor, according to some embodiments.

[0015] [Figure 5] FIG. 5 is a flow diagram illustrating a method for determining the state of semiconductor manufacturing equipment, according to some embodiments.

[0016] [Figure 6A] FIG. 6A is a flow diagram illustrating a method for determining a state of semiconductor manufacturing equipment, according to some embodiments. [Figure 6B] FIG. 6B is a flow diagram illustrating a method for determining the state of semiconductor manufacturing equipment, according to some embodiments.

[0017] [Figure 7A] FIG. 7A is a flow diagram illustrating a method for determining a state of semiconductor manufacturing equipment, according to some embodiments. [Figure 7B] FIG. 7B is a flow diagram illustrating a method for determining the state of semiconductor manufacturing equipment, according to some embodiments.

[0018] [Figure 8] FIG. 8 is a block diagram illustrating a hardware configuration for a multi-sensor fabrication tool (e.g., a multi-station fabrication tool) implementing a combination set of sensors utilizing multiple types of sensors for manufacturing equipment, according to some embodiments.

[0019] [Figure 9] FIG. 9 is an illustration of a cross-sectional view of an exemplary viewport of a fabrication tool (eg, the multi-sensor fabrication tool of FIG. 8), where the viewport has an associated set of sensors, according to some embodiments.

[0020] [Figure 10] FIG. 10 is a diagram of an external perspective view of a chamber for an exemplary viewport of a fabrication tool, according to some embodiments.

[0021] [Figure 11]FIG. 11 is a flow diagram illustrating a method for multi-sensor determination of process chamber cleaning endpoint, according to some embodiments.

[0022] [Figure 12A] FIG. 12A is a flow diagram illustrating a method for determining the presence of unexpected species in a process chamber, according to some embodiments. [Figure 12B] FIG. 12B is a flow diagram illustrating a method for determining the presence of unexpected species in a process chamber according to some embodiments.

[0023] [Figure 13] FIG. 13 is a schematic diagram of components of a computing device implemented in a computing system according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0024] This disclosure relates to characterizing semiconductor equipment, such as multi-station process chambers, using multi-sensing systems. Current approaches to sensing and monitoring the condition of semiconductor equipment typically involve using a sensor for each task or class of tasks. Measurements obtained from individual sensors are used alone to derive or infer one or more characteristics related to the equipment. In some cases, characteristics are determined using expert judgment based on experience, intuition, or results obtained from other sensors or techniques. Therefore, in some cases, a human element may be required in conjunction with the sensing techniques to obtain meaningful data.

[0025] In some typical process engineering scenarios, process performance checks are performed based on sensor responses to one of hundreds of correlation channels, for example, by visually verifying how the plasma behaves in a particular way when viewed through a viewport, or (most often) through performance on a substrate (e.g., a 300 mm wafer) after the process is complete. These approaches do not provide instantaneous responses. Engineers do not always have time to check the plasma for each recipe, and because plasma dynamics are on the order of microseconds (μs) to milliseconds (ms), subtle system drifts in rapidly changing systems may not be easily captured by visual inspection. Constant monitoring of correlation channels is not feasible without an automated system, and subtle changes may not be captured by current sensor implementations or may be considered noise in the system. The most reliable (and most frequently used) indicator of process change is the post-process impact of a specific recipe. Post-process metrology uses the response of on-wafer properties (e.g., deposited film thickness, refractive index (RI)) to indicate whether system conditions have changed. However, not all wafers are measured after processing. These sensing inefficiencies can lead to yield impact or wafer scrap, making real-time control unfeasible.

[0026] In some typical scenarios for cleaning endpoints (i.e., chamber cleaning endpoints), endpoint detection relies on timed cleaning (which does not account for system or process variability and / or accumulation changes based on different processes) or the use of IR-EPD. IR-EPD looks for a specific voltage and slope of the signal and adds an over-etching step. The over-etching step can result in significant etching in some areas of the station, potentially shortening the pedestal lifespan due to, for example, the formation of aluminum fluoride (AlF). Another method of endpoint detection in chamber cleaning involves the use of visual signals, for example, using a camera. However, such visual-based detection is limited to the visible region, and extensive validation is required to accurately determine which areas are cleaned slowest or last.

[0027] Therefore, there is a need for a sensing approach that can acquire and provide consistent and accurate signals in an automated manner to better understand the state of equipment systems and provide control opportunities.

[0028] The following terms are used throughout this specification:

[0029] "Manufacturing equipment" refers to equipment in which a manufacturing process is performed. Manufacturing equipment often has a process chamber in which a workpiece is placed during processing. Typically, during use, manufacturing equipment performs one or more semiconductor device fabrication operations. Examples of manufacturing equipment for semiconductor device fabrication include deposition reactors, such as electroplating cells, physical vapor deposition reactors, chemical vapor deposition reactors, and atomic layer deposition reactors, as well as subtractive process reactors, such as dry etch reactors (e.g., chemical and / or physical etch reactors), wet etch reactors, and ashers. In some embodiments, the manufacturing equipment may be a multi-station process chamber, for example, having four stations.

[0030] As referred to herein, manufacturing equipment may simply be referred to as a "process chamber." In various embodiments, a process chamber is typically a sealed enclosure in which a substrate is secured during processing. A process chamber may include components associated with gas delivery and removal. A process chamber may also include components associated with generating a plasma within the chamber and controlling plasma properties. A process chamber may include components for controlling pressure, including drawing a vacuum within the chamber. In the context of the present disclosure, a process chamber may include a pedestal upon which a substrate rests while it is being processed. The pedestal may be fitted with a chuck, such as an ESC, that holds the substrate in place during processing.

[0031] As used herein, a "semiconductor device fabrication operation" is an operation performed during the fabrication of a semiconductor device. As referred to herein, such fabrication operations may simply be referred to as a "process" or "treatment." Examples of treatments include depositing material onto a substrate, selectively etching material from the substrate, and ashing photoresist on the substrate. Typically, an overall fabrication process includes multiple semiconductor device fabrication operations, each performed in its own semiconductor fabrication tool, such as a plasma reactor, electroplating cell, chemical mechanical planarization tool, or wet etching tool. Categories of semiconductor device fabrication operations include subtractive processes, such as etching processes and planarization processes, as well as material-additive processes, such as deposition processes (e.g., physical vapor deposition, chemical vapor deposition, atomic layer deposition, electrochemical deposition, and electroless deposition). In the context of an etching process, a substrate etching process includes a process that etches a mask layer or, more generally, a process that etches any layer of material previously deposited on and / or potentially placed on a substrate surface. Such etching processes can etch a stack of layers in a substrate.

[0032] The terms "semiconductor wafer," "wafer," "substrate," "wafer substrate," and "partially fabricated integrated circuit" may be used interchangeably. Those skilled in the art will understand that the term "partially fabricated integrated circuit" can refer to a semiconductor wafer at any of many stages of integrated circuit fabrication. Wafers or substrates used in the semiconductor device industry typically have diameters of 200 mm, 300 mm, or 450 mm. In addition to semiconductor wafers, other workpieces that may utilize the disclosed embodiments include various articles such as printed circuit boards, magnetic recording media, magnetic recording sensors, mirrors, optical elements, display devices, or components such as backplanes for pixelated display devices, flat panel displays, micromechanical devices, etc. Workpieces may be of various shapes, sizes, and materials.

[0033] FIG. 1A illustrates a fabrication tool designated as a substrate processing apparatus 100. In various embodiments, the substrate processing apparatus 100 can be configured to deposit films on or over semiconductor substrates using any number of processes. For example, the substrate processing apparatus 100 may be configured to perform plasma-enhanced chemical vapor deposition (PECVD) or plasma-enhanced atomic layer deposition (PEALD). The substrate processing apparatus 100 may include one or more sensors or sensor packages 117 on the chamber walls. When implemented as a sensor package, the elements 117 may include two or more different sensors, which may be different types of sensors. In certain embodiments, the sensors include two or three of the following sensor types: spatial, spectral, and temporal. The sensor or sensor package 117 may be configured to capture image data from within the apparatus 100. Note that while the sensor or sensor package 117 is illustrated as a single block, it represents an implementation in which one, two, or more sensors are located in close proximity to each other and, optionally, share a single viewport or other window. In some cases, the individual sensors within the block representing the sensor or sensor package 117 are aimed at different components or fields of view within the chamber. In some cases, individual sensors in block 117 may be configured to capture different respective spectral ranges (far IR, near IR, visible light, UV, etc.).

[0034] 1A can employ a single process station 102 of a process chamber having a single substrate holder 108 (e.g., pedestal) within an interior volume, which can be maintained under vacuum by a vacuum pump 118. A showerhead 106 and gas delivery system 101 fluidly coupled to the process chamber can enable delivery of, for example, film precursors, as well as carrier gases and / or purge gases and / or process gases, secondary reactants, etc.

[0035] In FIG. 1A , the gas delivery system 101 can include a mixing vessel 104 for blending and / or conditioning process gases for delivery to the showerhead 106. One or more mixing vessel inlet valves 120 can control the introduction of process gases into the mixing vessel 104. Certain reactants may be stored in liquid form before being vaporized and subsequently delivered to the process stations 102 of the process chamber. The embodiment of FIG. 1A can include a vaporization point 103 for vaporizing liquid reactants delivered to the mixing vessel 104. In some embodiments, the vaporization point 103 can include a heated liquid injection module. In some other embodiments, the vaporization point 103 can include a heated vaporizer. In still other embodiments, the vaporization point 103 can be eliminated from the process station. In some embodiments, a liquid flow controller can be provided upstream of the vaporization point 103 to control the mass flow rate of the liquid that is vaporized and delivered to the process station 102.

[0036] The showerhead 106 operates to deliver process gases and / or reactants (e.g., film precursors) to the substrate 112 in the process stations, the flow of which may be controlled by one or more valves (e.g., valves 120, 120A, 105) upstream from the showerhead. In the embodiment illustrated in FIG. 1A, the substrate 112 is shown positioned below the showerhead 106 and resting on a pedestal 108. The showerhead 106 may include any suitable shape and any suitable number and arrangement of ports for delivering process gases to the substrate 112. In some embodiments involving two or more stations, the gas delivery system 101 may include valves or other flow control structures upstream from the showerhead to independently control the flow of process gases and / or reactants to each station, allowing gas flow to one station while prohibiting gas flow to a second station. Additionally, the gas delivery system 101 may be configured to independently control the process gases and / or reactants delivered to each station in a multi-station apparatus, thereby allowing the gas compositions provided to different stations to differ, e.g., partial pressures of gas components may vary between stations at the same time.

[0037] In the embodiment of FIG. 1A , the gas volume 107 is illustrated as being located below the showerhead 106. In some embodiments, the pedestal 108 may be raised or lowered to expose the substrate 112 to the gas volume 107 and / or to change the size of the gas volume 107. The separation between the pedestal 108 and the showerhead 106 is sometimes referred to as a “gap.” Optionally, the pedestal 108 may be lowered and / or raised during portions of the deposition process to adjust the process pressure, reactant concentration, etc., within the gas volume 107. The showerhead 106 and pedestal 108 are illustrated as being electrically coupled to an RF signal generator 114 and a matching network 116 for coupling power to the plasma generator. Thus, the showerhead 106 can function as an electrode for coupling radio frequency power to the process station 102. The RF signal generator 114 and the matching network 116 can operate at any suitable RF power level, which can be operated to form a plasma having a desired composition of radical species, ions, and electrons. Additionally, the RF signal generator 114 may provide RF power having multiple frequency components, such as a low frequency component (e.g., below about 2 MHz) and a high frequency component (e.g., above about 2 MHz). In some implementations, plasma ignition and maintenance conditions may be controlled by appropriate hardware and / or appropriate machine-readable instructions in a system controller, which may provide control instructions via a series of input / output control instructions.

[0038] In general, the disclosed embodiments can be implemented with any plasma-assisted fabrication tool that includes integration of a sensor (e.g., including a sensor or sensor package 117) configured to acquire data (e.g., including images) regarding the plasma and / or plasma-related phenomena. Exemplary deposition equipment includes, but is not limited to, equipment from the ALTUS® product family, VECTOR® product family, and / or SPEED® product family, KIYO® product family, STRIKER® product family, and VERSYS® product family, each available from Lam Research, Inc. of Fremont, California, or any of a variety of other fabrication tools that use plasma.

[0039] Additionally, in some embodiments, the sensors or sensor packages described herein may be capable of serving multiple purposes. As one example, a sensor or sensor package may be or include a hyperspectral sensor (configured to capture intensity values ​​at multiple narrow wavelength bins) or a multispectral sensor (configured to capture intensity values ​​across a wider wavelength bin, typically fewer than the multiple narrow bands of hyperspectral sensing). A hyperspectral or multispectral sensor may thereby provide both spatial and spectral sensor information, where the spectral information may correspond to wavelengths outside the visible spectrum (e.g., at least a portion of the ultraviolet (UV) spectrum, at least a portion of the infrared (IR) spectrum). As another example, a sensor may be sophisticated enough to provide the functionality of any two or more types of sensor. For example, a camera with a very fast frame rate (e.g., 120 frames per second or greater) may capture spatial and temporal information and thus serve as both a spatial and a temporal sensor.

[0040] Referring now to FIG. 1B, one implementation of a multi-station fabrication tool 150 is illustrated, according to some embodiments. For simplicity, the processing apparatus 100 is illustrated in FIG. 1A as a stand-alone process chamber station 102 for maintaining a low-pressure environment. However, some fabrication tools employ multiple process stations, as shown in FIG. 1B. In some embodiments, the multi-station fabrication tool 150 can employ a process chamber 165 that includes multiple fabrication process stations, each of which can be used to perform a processing operation on a substrate held on a wafer holder, such as the pedestal 108 in FIG. 1A, at a particular process station. In the embodiment of FIG. 1B, the process chamber 165 is shown as having four process stations 151, 152, 153, and 154. However, in certain other embodiments, the multi-station processing apparatus can have a greater or lesser number of process stations, depending on the implementation and, for example, the desired level of parallel wafer processing, size or space constraints, or cost constraints. FIG. 1B also illustrates a substrate handler robot 175, which can operate under the control of a system controller 190. The substrate handler robot 175 can be configured to move substrates from a wafer cassette (not shown in FIG. 1B) from the load port 180 to the multi-station process chamber 165 and to any of the process stations 151, 152, 153, or 154.

[0041] 1B , process station 153 has an associated sensor or sensor package 121 positioned and configured to obtain in situ information (e.g., image, spectral, and / or temporal data) from within process station 153, and in some embodiments, from within process chamber 154. Process station 151 may have two associated sensors or sensor packages 123 and 125. Sensor or sensor package 123 is positioned and configured to obtain in situ information from within process station 151, and in some embodiments, from within process chamber 152. Sensor or sensor package 125 is positioned and configured to obtain in situ information from within process station 151, and in some embodiments, from within process chamber 153. Process station 152 may have an associated sensor or sensor package 127 positioned and configured to obtain in situ information from within process station 152, and in some embodiments, from within process chamber 154. When implemented as a sensor package, elements 121, 123, 125, and 127 may include two or more different sensors, which may be different types of sensors. Any one or more of sensors or sensor packages 121, 123, 125, or 127 may be coupled to the interior of process chamber 165 through a viewport or other window located in the chamber wall. Additionally, although not shown in FIG. 1B , some embodiments may include one or more sensors or sensor packages adjacent to process station 154. Exemplary positioning of sensors or sensor packages is further described with respect to FIGS. 2 and 3 below. Furthermore, as described in more detail below, each sensor or sensor package may be at least one spatial sensor, at least one spectral sensor, or at least one temporal sensor, e.g., a combination of two or more of these types of sensors.While some sensors may be standalone sensors of one type, this disclosure describes applications for sensors that incorporate multiple types to increase measurement accuracy, precision, stability, and completeness.

[0042] In the context of this disclosure, the terms precision, stability, and consistency may refer to the sigma (σ, standard deviation) of a process metric (measurement result) reported by a sensor or sensor package in high-volume manufacturing (HVM). Ostensibly, the same process is measured repeatedly over time using multiple copies of the sensor or sensor package (measurement equipment). The same sensor package may be installed at multiple stations within a process module, or may be installed in multiple process modules that are installed on multiple tools and possibly multiple HVM lines in different locations. These in-line HVM process metrics from the sensor or sensor package can be used to develop correlations with other HVM process metrics, including those obtained using precise, offline, dedicated metrology techniques. Such correlations may enable near-real-time process optimization and root-cause analysis of process deviations.

[0043] Measurement results or metrics may be recorded by the host device (e.g., a computing device) receiving the data. Precision can be defined as the sigma of a metric repeated under nearly identical conditions over the shortest possible time. Precision can quantify or represent the fundamental limits of a metric; typically, calculating the sigma requires using more than 10 input values ​​of the metric, where values ​​are obtained in rapid succession without unnecessary interruptions. Stability can be defined as the sigma of a metric reported over a defined period. Stability can quantify or represent process studies or gauge studies (which indicate the repeatability and reproducibility of measurements), which are typically conducted over multiple days, such as the life of a process kit. Process stability and gauge stability may not be easily separable in data for many metrics. Careful use of stability results across multiple metrics can help distinguish gauge drift from process drift. High stability is beneficial because predictive maintenance and process throughput improvements must be based on metrics with known stability. Consistency can be defined as the sigma of a metric across multiple sensors or sensor packages acquired over multiple days (sensor subsystem stability data set). One example of matching is within a single process module (PM) containing multiple sensor subsystems. In particular, intra-PM matching can quantify the PM matching sigma of a metric using multiple subsystems on a single PM. Inter-PM matching can measure matching, for example, within a fabrication tool, between fabrication tools, or across an installed base at multiple sites. While matching nonidealities can be managed using subsystem-specific control limits (e.g., of the sensor or sensor package), this approach is cumbersome. Metrics that provide good matching between properly functioning subsystems are preferred. In general, process precision is easiest (single chamber, short time), process stability is difficult to achieve (e.g., due to drift over time), and chamber-to-chamber or tool-to-tool process variation is the most challenging.

[0044] In some embodiments, a sensor or sensor package may report metrics to a host device as a function of time. This data can be further refined to define customized metrics by the end user of the sensor or sensor package. Metric precision, stability, and consistency help define the limits of the sensor package for managing the underlying process. Ultimately, process precision, process stability, and process consistency are desirable for a sensor or sensor package, and are used, for example, to develop process metrology techniques, perform process quality control (e.g., establish whether behavior is normal, whether abnormal behavior is an impulse or drift issue, and whether behavior warrants corrective action), and establish process tolerance limits for metrics that the end user deems important.

[0045] In some embodiments, fabrication tool 150 may include a system controller 190 configured to control process conditions and hardware states of fabrication tool 150. In some embodiments, system controller 190 may interact with one or more sensors, gas flow subsystems, temperature subsystems, and / or plasma subsystems (collectively represented as a block representing subsystem 191) to control appropriate process gas flows, thermal conditions, and / or plasma conditions to control the fabrication process. In various embodiments, system controller 190 and subsystems 191 may act to implement recipes or other process conditions in one or more of the process stations (e.g., 151-154) of process chamber 165. The system controller may be located entirely on the fabrication tool or in close proximity to the fabrication tool (e.g., as an edge computer within the fabrication equipment), or the system controller may be located entirely remotely from the fabrication tool (e.g., on a hosted cloud computing resource), or the system controller may be located partially on the fabrication equipment and partially remotely.

[0046] In a multi-station fabrication tool, the RF signal generator may be coupled to an RF signal distribution unit configured to split the power of an input signal into, for example, four output signals. The output signals from the RF signal distribution unit may have similar levels of RF voltage and RF current, which may be communicated to individual process stations (e.g., 151-154) of the multi-station fabrication tool.

[0047] FIG. 1C shows a top view of an electronic device fabrication system 182 having four quad-station fabrication tools 188, 189, 193, and 195. The quad-station fabrication tools 188, 189, 193, and 195 may be examples of stations 151-154 in FIG. 1B. Each quad-station tool includes four process stations, each configured to hold and process substrates. At the front end of the electronic device fabrication system 182 are three front-opening unified pods (FOUPs) 183a, 183b, and 183c accessible by a front-end wafer-handling robot 185 configured to transfer wafers between the FOUPs and a first load lock 187. A first wafer handler 170 may be located and configured to transfer wafers between the first load lock 187 and the quad-station fabrication tools 188 and 189. The first wafer handler 170 may also be configured to transfer wafers to a second load lock 171, which makes the wafers available to the quad station fabrication tools 193 and 195 via a second wafer handler 172.

[0048] In some embodiments, quad station tool 195 (as an example) may include three sensors or sensor packages 196, 197, and 198 arranged around its exterior walls. In FIG. 1C, sensors or sensor packages 196-198 are shown vertically affixed to three sides of the four-sided chamber of tool 195. In the illustrated embodiment, the only side lacking a sensor or sensor package is the side next to wafer handler 172. Although not shown in FIG. 1C, a similar sensor or sensor package arrangement may be provided in any one or more of each of the three other quad station chambers 188, 189, or 193 in the system. Furthermore, in some implementations, a sensor or sensor package may still be mounted on the side next to wafer handler 172. FIGS. 2 and 3 illustrate different possible sensor or sensor package locations. Countless other positionings of one or more sensors are possible, including combined or separate combinations of sensors within a sensor package, depending, for example, on the measurement, angle, and location desired. It should be understood that in some cases, the system controller (e.g., 190 in FIG. 1B) may be configured to modify the position or orientation (e.g., up, down, left, right, diagonal, azimuth, elevation) of a given sensor or sensor package.

[0049] 2 and 3 schematically illustrate fabrication tools having sensors or sensor packages oriented to capture information (e.g., spatial, spectral, and / or temporal) from horizontal and vertical lines of sight, respectively. In some embodiments, each described sensor or sensor package may include two or more types of spatial, spectral, or temporal sensors of the types described below, combined such that each of the types can obtain information and measurements sequentially (e.g., based on trigger conditions that are or are not met by one sensor type) or in parallel (e.g., two or more sensor types gather information simultaneously).

[0050] FIG. 2 illustrates a process chamber 270 having chamber walls 271, a showerhead 272, and a pedestal 273, which may be designed and constructed in any manner known in the art; FIG. 2 merely serves to illustrate an exemplary position and viewing angle of an exemplary sensor or sensor package 274. FIG. 2 also illustrates an exemplary sensor or sensor package 274 positioned to capture in situ information from inside the process chamber 270 through a viewport or window 276 (e.g., a sapphire rod having a diameter of 1-10 mm) designed for sensor or sensor package access. The sensor or sensor package 274 may have a field of view defined by edges 277 and 278. As shown, the sensor or sensor package 274 and window 276 are positioned to enable the sensor 274 to capture, for example, a plasma image including the vertical edge of the pedestal 273, spectral information associated therewith, and / or temporal information associated therewith. Other arrangements may enable the sensor or sensor package to capture thermal images of other vertical edges within the process chamber 271, as well as other information such as pressure, temperature, voltage, current, or other measurements of the process chamber or a particular station, depending on whether sensors capable of capturing such information are incorporated into the sensor or sensor package 274.

[0051] FIG. 3 illustrates a similar process chamber 381, but with exemplary sensors or sensor packages 394 and 394′ having a vertical line of sight to enable them to capture information of other features within the process chamber 381. In certain embodiments, the process chamber 381 may have a chamber wall 379, a showerhead 384, and a pedestal 386, all of which may be designed and constructed in any manner known in the art, with FIG. 3 serving only to illustrate exemplary positions and viewing angles of the exemplary sensors or sensor packages 394 and 394′. FIG. 3 illustrates vertically oriented sensors or sensor packages 394 and 394′ positioned to capture in situ information from the interior of the process chamber 381 through viewports or windows 399 and 399′ designed for sensor or sensor package access. The sensor or sensor package 394 may have a field of view defined by edges 361 and 362, and the sensor or sensor package 394′ may have a field of view defined by edges 363 and 364. As shown, the sensor or sensor package 394 and window 399 may be positioned to allow the sensor or sensor package 394 to capture a plasma image that includes the stem or underside of the pedestal 386. As shown, the sensor or sensor package 394' and window 399' may be positioned to allow the sensor or sensor package to capture a plasma image that includes, for example, the edge of the stem and / or the backside of the showerhead 384, spectral information associated therewith, and / or temporal information associated therewith. Other arrangements may allow the sensor or sensor package to capture thermal images of other edges within the process chamber 381, as well as other information such as pressure, temperature, voltage, current, or other measurements of the process chamber or particular stations, depending on whether sensors capable of capturing such information are incorporated into the sensor or sensor package 394 and 394'.

[0052] The sensor or sensor package can be located outside the fabrication tool, but in some embodiments, it can also be integrated with the chamber wall or other components or assemblies within the chamber, as shown in Figures 9 and 10. In certain embodiments, a viewport or window specially constructed for the sensor can be integrated into the chamber wall. In certain embodiments, the sensor or sensor package can be coupled to the interior of the fabrication tool using an access opening in or on the chamber wall to allow visual inspection of the tool interior.

[0053] Sensors and Sensor Configurations In various embodiments of the present disclosure, multiple sensors, such as two or more of at least one spatial sensor (e.g., a camera), at least one spectral sensor (e.g., an optical emission spectroscopy (OES) sensor), and / or at least one temporal sensor (e.g., a photodiode), are implemented into an integrated sensor “package” or sensor system and can be used for various applications and tasks in semiconductor systems, such as fabrication tools that may include one or more process chambers and / or stations (e.g., multi-station fabrication tool 150, fabrication system 182). Such sensors may be positioned or configured to sense plasma or monitor conditions in situ (without moving the wafer from a processing chamber to a separate metrology chamber after processing for sensing or monitoring) and in real time (i.e., on a timescale comparable to the timescale of events occurring in the process chamber while a process is being performed on the wafer). For these purposes, the sensors may be integrated with a viewport or other window providing visual access into the process chamber, as described with respect to FIG. 8 below.

[0054] In disclosed embodiments, such sensor packages may enable monitoring equipment or personnel to solve some of the problems currently faced by a given piece of semiconductor equipment by providing two or more of the spatial, spectral, or temporal information from each type of sensor. Such spatial, spectral, and / or temporal information, when combined and analyzed (e.g., using combinatorial likelihood, machine learning models, or other algorithms), can provide a better understanding of the state of the system associated with the semiconductor equipment. The information may also provide control opportunities that result in benefits such as cost reduction, improved machine availability (MA, the length of unit time a machine is available to process, e.g., wafers), improved green-to-green (GtG) duration (reduced downtime periods) and throughput, improved wafer-to-wafer (WtW) uniformity and wafer quality, and increased precision, stability, and consistency by utilizing a combination of different types of sensors that were previously used independently. In various use cases, different sensors can serve as primary, secondary, and / or tertiary indicators. In some implementations, optionally, in addition to the spatial, spectral, and / or temporal sensors, other sensors in the monitoring system may take readings including voltage and current (VI), pressure, temperature, etc.

[0055] Space Sensor In various embodiments, the spatial sensor may be an image capture device (e.g., a camera or camera sensor) configured to acquire visual or optical information (e.g., image data, time-of-flight (TOF) data, or both) to provide at least a two-dimensional radiation intensity image (e.g., a 2D area array of image data having x x y pixels). The spatial sensor can provide images of features within the chamber, such as plasma characteristics. Plasma characteristics can include plasma boundaries, plasma location, and whether the plasma is anomalous or parasitic. The spatial sensor can also provide images of chamber component boundaries and gas flow patterns (e.g., over time or at a given moment).

[0056] A camera sensor, an example of a spatial sensor, may be characterized by various parameters, including the number of pixels, the range of wavelengths captured, etc. In some embodiments, a camera sensor for capturing information about a plasma may be capable of multispectral or hyperspectral imaging and detecting intensity values ​​across the entire electromagnetic spectrum, including visible radiation at wavelengths that include at least a portion of the visible spectrum. As an example, the camera sensor may be configured to detect intensity values ​​across one or more ranges, including, for example, 100 nm to 1000 nm. In some embodiments, the camera sensor may distinguish signals from wavelengths in at least a portion of the visible spectrum, at least a portion of the UV spectrum, at least a portion of the IR spectrum, or any combination thereof. As an example, a camera sensor that captures both IR and visible information can provide information associated with thermal processes as well as plasma processes.

[0057] As an example for any embodiment herein, the camera sensor can be configured as a charge-coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) array. In some implementations, quantum image sensors (QIS) and single-photon avalanche diode (SPAD) array detectors for imaging and time-of-flight may be included as variations within the CMOS array. In certain embodiments, the camera sensors used herein may have at least about 5 megapixels or at least about 12 megapixels. In some embodiments, the camera sensors used herein may have only about 2 megapixels. Multispectral sensors can use a color filter array (CFA) tiled across the detector array. Another type of array tiled across the camera's detector array can be a polarization filter array (PFA). Polarization images can enable spatially resolved thin film stress detection, among other applications.

[0058] In some implementations, the image capture device is a line or one-dimensional array of sensors or pixels. Such a device may be configured to scan across a two-dimensional field of view. The scanning direction may be substantially perpendicular to the axis of the line of sensors. In some embodiments, the one-dimensional image capture device is oriented perpendicular to the wafer or chamber components and is optionally configured to scan from one side of the chamber to the other (or to other portions or fields of view within the chamber). In some implementations, the image capture device includes a multidimensional array of detectors (e.g., a two-dimensional array of detectors). The image capture device may be configured to access a multi-dimensional field of view within the process chamber.

[0059] In certain embodiments, a camera used in any of the embodiments herein may be configured with a shutter. In some embodiments, the camera may be configured to capture video data of the plasma within the fabrication tool. In certain embodiments, the camera may be configured to capture video information of the plasma within the fabrication tool at a frame rate of about 30-120 frames / second, or about every 1-100 ms. In some embodiments, the camera need not provide a high capture rate (e.g., about every 1 ms or more frequently) and can have a capture rate lower than standard video, for example, less than about 30 frames / second, less than about 10 frames / second, or less than about 1 frame / second. In some implementations, frames may be captured manually using a shutter.

[0060] Some fabrication tools include still image or video displays. Such displays can be used to allow process engineers or other staff to view the interior of the tool when camera sensors or light conduits block access to viewports from outside the tool. In some embodiments, views of the chamber interior are provided via electronically streamed images or video (e.g., using Real Time Streaming Protocol (RTSP), Real Time Messaging Protocol (RTMP), Low Latency HTTP Live Streaming (HLS), Secure Reliable Transport (SRT), WebRTC, etc.), optionally provided to a remote location via a web application. Examples of remote sites include a fab monitoring room or facility, smartphones, tablets, and / or desktop computer systems. In some embodiments, communication of images or video occurs over a network that includes cameras on the process chambers as nodes. Such a network can be wired or wireless, e.g., a mesh network. In certain embodiments, the network can employ communication protocols using Wi-Fi, Bluetooth, cellular, etc.

[0061] In some embodiments, the image analysis logic may be configured to receive sensed values ​​from one or more camera sensors on the fabrication tool. In particular embodiments, input to the image analysis logic includes intensity values ​​per pixel as a function of an observable parameter, such as wavelength, time, polarization, or any combination thereof. In particular embodiments, the input data from the camera sensors is provided in the form of image data, video data, spectral values, time series data, wafer metrology data, etc. In some embodiments, the input data is filtered by wavelength, polarization, etc. In some embodiments, the analysis logic is configured to receive and act upon additional input information other than the camera sensor intensity data. Such additional input information may include metadata about the camera sensors and / or associated camera components, substrate metrology information, historical information about the fabrication tool, etc.

[0062] The analysis logic may be configured to output a classification of one or more properties of the plasma in the fabrication tool and / or the condition of the fabrication tool or its components. In some cases, the analysis logic may be configured to perform image processing routines, such as segmentation or other edge-finding routines. In certain embodiments, the analysis logic may be configured to use segmentation or other edge-detection methods to determine plasma property information for system components.

[0063] Additionally, in some embodiments, the camera sensor analysis logic may include any of various types of classifiers or models, such as deep neural networks (e.g., convolutional neural networks, autoencoders, U-Nets), edge detection, image modification (blurring, contrast changes, etc.), intensity thresholding, color channel thresholding, and other traditional or classical computer vision methods.

[0064] The logic may use any of a variety of techniques for edge detection or segmentation. For example, the logic may use threshold-based methods, deep learning models, etc. In some embodiments, the edge of the plasma or the boundary of a subregion within the plasma having defined plasma characteristics may be determined.

[0065] Spectral Sensor Spectral sensors provide wavelength-specific radiation intensity detection. They output radiation intensity values ​​as a function of wavelength or spectral region. Spectral sensors have a variety of capabilities. The information provided can distinguish or identify specific chemical species, each of which has its own specific spectrum. In some embodiments, spectral sensors can distinguish signals from wavelengths in one or more regions of the electromagnetic spectrum, including UV, visible light, and IR.

[0066] Spectral sensors can be implemented in a variety of ways. Examples of wavelength separation components include: (a) dispersive devices (which separate wavelengths with a dispersive medium such as a prism), (b) diffractive devices (which separate wavelengths by diffraction using, for example, a diffraction grating), and (c) filters placed in front of an intensity detector so that the detector receives only specific wavelengths of interest.

[0067] An example of a spectral sensor detector is a linear array of photodetectors configured to detect wavelength-specific intensities at different detectors in the linear array. The photodetectors can be configured to provide an intensity output as a function of wavelength. A wavelength separation component positioned before the linear array provides wavelength separation in one dimension (along the linear array), such that each element of the linear detector is associated with a particular wavelength or range of wavelengths.

[0068] Unlike spatial sensors, spectral sensors do not need to provide multidimensional images. Additionally, in some applications, the speed at which a spectral sensor acquires data does not need to be fast (e.g., an acquisition rate of ≥ 1 ms or about 1 ms to 1 s).

[0069] In certain embodiments, the photodetector for the OES sensor is a linear one-dimensional or two-dimensional array of detector devices. As an example, the OES sensor is a two-dimensional charge-coupled device (2-D CCD) array in which spectrally separated light components are detected by different regions of the 2-D CCD. In some implementations, a linear array of detectors may be used in which different detectors in the linear array separately detect wavelength-specific intensities. Refraction (e.g., a prism) or diffraction (e.g., a grating) may be used for wavelength separation in one dimension along the linear array. Each element of the linear detector may be associated with a specific wavelength or range of wavelengths. Various implementations may be used for different use cases. In some embodiments, an OES sensor for capturing spectral information about a plasma may be capable of sensing intensity values ​​of and / or distinguishing signals from electromagnetic radiation at wavelengths including at least a portion of the visible spectrum, at least a portion of the ultraviolet (UV) spectrum, at least a portion of the infrared (IR) spectrum, or any combination thereof. In some cases, the photodetector need not provide a fast capture rate (e.g., about every 1 ms or more). The photodetector can operate at a frame rate of, for example, about 1 ms to 1 second.

[0070] FIG. 4 is a diagram of an exemplary OES sensor 400, according to some embodiments. The OES sensor 400 may be coupled to one or more transmission optical fibers 402. The transmission optical fibers 402 may collect spectral information, for example, from a corresponding process in a process chamber or a station within a process chamber. For example, the transmission optical fibers 402 may receive spectral information from a process station in a multi-station process chamber (e.g., of the type described with reference to FIGS. 1B and 1C) via light collection optics 403 during the process. In some embodiments, four optical fibers 402 are each associated with a unique process station and collect optical signals emitted from that process station. The transmission optical fibers 402 may be bundled and received by the OES sensor 400. As shown in FIG. 4, the OES sensor 400 may receive spatial information from four stations in a four-station process chamber via four transmission optical fibers 402, as one exemplary implementation.

[0071] The OES sensor 400 may further include dispersive optics, such as one or more mirrors 404, some or all of which may have optically dispersive properties by being coated at various depths so that different wavelengths have different penetration lengths, allowing the mirrors to reflect different wavelengths of light. The OES sensor 400 may include an image sensor 406 that detects spatial information, which may include spectrally separated light components.

[0072] Countless other implementations of the OES sensor are possible, such as mounting the OES sensor directly at the viewport, eliminating at least a portion of the transmission optical fiber 402, eliminating some or all of the light collection optics 403, switching between different light inputs (e.g., fiber switches, microelectromechanical systems (MEMS) mirrors), using different types of dispersive optics configurations (e.g., gratings, prisms, computed tomography (CT), echelle gratings), using spectral filters (e.g., filter wheels, or possibly etalons or tunable liquid crystals), or using time-domain spectral analysis (e.g., possibly using an optical spectrum analyzer).

[0073] In some embodiments, a spectral reflectometer device may be used as the spectral sensor. The reflectometer device may include a light source used to illuminate the surface of interest and a photodetector. The photodetector may include one or more photodetectors. A fiber optic cable may be connected to the spectral reflectometer device. The fiber optic cable may include a transmission optical fiber and a receiving optical fiber, and each receiving optical fiber may be connected to a respective photodetector. In some cases, multiple receiving optical fibers may be connected to the same photodetector.

[0074] In some embodiments, phase-sensitive spectroscopic ellipsometry can be used. In some embodiments, polarization-controlled reflectometry may be used, especially in highly polarized structures.

[0075] It should be noted that while spatial information may be based on the intensity collected by each pixel of a sensor (eg, a camera), spectral sensor intensity counts may be collected over a larger area.

[0076] Time Sensor A temporal sensor can detect and measure the temporal characteristics of pulsed or other bursts of light (e.g., an arc forming in a plasma system). In environments such as process chambers, temporal sensors can be used to very quickly identify process changes due to their fast response times (e.g., within approximately 100 ms, or picoseconds (ps) to microseconds (μs), e.g., 10 ps to 100 ms). By measuring the temporal characteristics (e.g., the temporal variation of light emission as an electrical signal), the temporal sensor can measure and / or provide signal data of such process changes. Examples of temporal sensors include photodiode light sensors, microphotodiodes, phototransistors, photocells, pulse characterization sensors, photomultiplier tubes, solid-state photomultiplier tubes, or other high-speed one-dimensional radiation (typically, but not necessarily limited to, visible light) sensors suitable for use in process chambers or multi-station fabrication tools (e.g., 150, 182) of the types described above. The temporal sensor may be configured to acquire a single data point in the spatial domain. In some implementations, optical filters or lenses may be used in combination with the temporal sensor.

[0077] In embodiments using photodiodes or arrays thereof, for example, the photodiode includes at least one semiconductor junction (e.g., pn, np, pnp, npn, ppn, etc.) where light striking the junction can create electron-hole pairs. As known to those skilled in the relevant art, the electron-hole pairs can migrate to different portions of the junction and generate voltage and / or current (when connected in a circuit). In this manner, it is possible to detect substantially instantaneous light or even bursts of light or photons (e.g., on the aforementioned nanosecond to μs scale). Furthermore, there is no need to distinguish between wavelengths or obtain multidimensional images.

[0078] Variations in the architecture of a photodiode or time sensor, such as increasing the width of the depletion region of the photodiode, the shape of the photodiode mesa, the number and depth of n- or p-layers utilized, the dopants utilized, the junctions utilized, or the amount of bias applied across the junctions, may result in modifications to the detectability of process changes, or indicators of process changes (e.g., a light-emitting diode that does not emit light based on a process change or that activates when visible light falls below a certain level), as needed or desired by a particular application (which may be one or more of a number of sensors, as described further below in this disclosure describing a combination set of sensors), for example, via changes in quantum yield (the ratio of emitted photons to absorbed photons) or sensitivity (the ratio of current flow to optical irradiance). The range of radiation detected may vary depending on the materials used, as known by those skilled in the relevant art; an example of a wavelength detected may include 300-1200 nm. In some variations, the photodiode may be a PN photodiode (having P-type and N-type layers), a PIN photodiode (having an inherent "I region"), a Schottky photodiode, or an avalanche photodiode. Arrays of the foregoing may also be used.

[0079] In some embodiments, a given process chamber, multi-station fabrication tool, or its process chambers may include one or more spatial sensors, one or more spectral sensors, one or more temporal sensors, or a combination thereof. The sensors may be located inside or outside the chamber. The sensors may be mounted on an internal or external surface of the chamber, or on a platform, or other component, and may be configured for signal or data communication with, for example, controller 190 or subsystem 191.

[0080] In some embodiments, one or more additional or alternative sensors may be used that are configured to detect or measure other parameters within or associated with the process chamber, such as VI, pressure, or temperature.

[0081] lighting source In some cases, the fabrication tool may include an illumination system or one or more illumination sources configured to illuminate all or one or more portions of the interior of the fabrication tool. In some implementations, the illumination system may be configured to allow a camera to capture illuminated images when the plasma is off (e.g., between external operations or pulses). Note that in some cases, an illumination system is not used, and illumination from the plasma itself is used. In some implementations, the illumination system uses one or more light-emitting diodes (LEDs) or other light sources. The light sources may be monochromatic, polychromatic with discrete emission wavelengths, or broad spectrum. The light sources may be continuously active, pulsed synchronously with the operation of spatial sensors (e.g., one or more camera shutters), pulsed asynchronously with the operation of spatial sensors (e.g., one or more camera shutters), or pulsed synchronously with other process parameters such as RF generators or gas delivery valves. In some cases, pulsing the light source may facilitate extracting and providing details about the process ongoing within the fabrication tool. For example, the light can be pulsed synchronously with optical sensing (e.g., by closing and opening a camera shutter to capture image data, e.g., optical images or video of visible effects and events, e.g., plasma behavior and properties), while spectral sensing is not performed during the pulsed illumination period so as not to corrupt the spectral signal captured during optical imaging. However, pulsing the light source asynchronously with optical sensing (e.g., camera shutter operation) may enable the capture of other visual information, such as the location of a plasma arc, which can be better detected and located in the dark than when the chamber is illuminated. In some embodiments, a time sensor can additionally or alternatively be used to detect the occurrence and presence of an arc when the chamber is not illuminated by the pulse. It is beneficial to detect arcs or other artifacts or anomalies that may be damaging to components, which may be more difficult to identify or locate when the chamber is illuminated by the pulse.These exemplary illumination pulse schemes increase the efficiency and quantity of information extracted from ongoing processes and are particularly useful for short-lived (e.g., ms- or ps-scale, as described above) events and process changes occurring within the fabrication tool. In other embodiments, multiple light sources are used at different locations inside or outside the chamber. These multiple light sources may be energized continuously or sequentially in a timed manner, allowing for the use of structured illumination to build super-resolution images of features within the chamber. In some embodiments, one or more notch or bandpass filters are placed in front of the light sources to provide effects that can support analysis (e.g., identification of specific chemical species by emission spectrum). In some embodiments, strobe lighting and other consistent structured illumination (e.g., using periodic bursts of illumination at 10, 30, or 60 Hz) can be used to provide additional, distinct visual information at a consistent frequency.

[0082] Additional applications for illumination sources include illuminating internal components of a process chamber that would otherwise be dark, thereby enabling a spatial sensor to image such components. Images of these components can provide a frame of reference for locating other features within the chamber, such as the location of the plasma relative to the gap between the pedestal and the showerhead, or the location of the plasma relative to the chamber walls. Images of adjacent stations in a multi-station chamber can also be enabled. The location of the adjacent stations can also provide a frame of reference for the key feature being imaged at the station of interest.

[0083] Illumination sources can also be used as "health checks" to verify or confirm that systems and subsystems, such as spatial, spectral, and temporal sensors, are functioning or operating normally and correctly, to track system drift over time, and to determine whether the sensor or illumination source is the cause of system degradation. Illumination sources can also be used as a calibration mechanism for sensors. For example, strobe illumination of specific wavelengths may support the calibration of spectral OES sensors. In some cases, OES calibration to specific wavelengths of light can be done via software to correct for shifts. In some approaches, illumination sources can also be used to determine the sensitivity of optical detectors (e.g., photodetectors) and adjust threshold offsets to minimize false detections if the photodiode itself shifts over time.

[0084] method FIG. 5 is a flow diagram illustrating a method 500 for determining a state of semiconductor manufacturing equipment, according to some embodiments. One or more of the functions of method 500 may be performed by or caused by a computerized device or system. The structure for performing the functionality illustrated in one or more of the steps shown in FIG. 5 may include hardware and / or software components of such a computerized device or system, such as, for example, a controller device, a computerized system, or a computer-readable device including a storage medium storing computer-readable and / or computer-executable instructions that, when executed by a processor device, are configured to cause at least one processor device or computerized device to perform an operation. A controller may be an example of a computerized device or system. A subsystem (e.g., 191) may be an example of a computerized device or system. A process chamber may be another example of a computerized device or system. Exemplary components of a process chamber and a controller are shown in FIGS. 1A and 1B, and 13, respectively, and are described in more detail elsewhere herein.

[0085] It should also be noted that the operations of method 500 may be performed in any suitable order, not necessarily in the order illustrated in Figure 5. Furthermore, method 500 may include additional or fewer operations than those illustrated in Figure 5 to determine the state of semiconductor manufacturing equipment.

[0086] As described above, when implementing the present disclosure, opportunities arise for controlling a fabrication tool, for example, when using method 500. Managing process parameters and controlling a fabrication tool can be approached in three stages: (1) detection, (2) recognition, and (3) control (e.g., changing setpoints, desired values, or target values). An integrated set of sensors, such as the multi-sensor system described with respect to FIGS. 8-10, can implement method 500 to augment, clarify, or replace traditional plasma monitoring, film growth, and mechanical or wafer movement sensor devices. Recent needs have revealed insufficient monitoring of chamber conditions and expected mechanical movement. For these purposes, a multi-sensor system can enable proactive monitoring and control of chamber conditions based on plasma, parasitics, HCD, particles, process drift, and mechanical variations in the gap and / or around the pedestal or showerhead (as well as other use cases discussed above) and can support real-time analytical capabilities in an integrated package.

[0087] In block 510, method 500 may include determining a set of signals to monitor from a plurality of sensors of the semiconductor manufacturing equipment during a process performed by the semiconductor manufacturing equipment. In some embodiments, block 510 may involve setting data acquisition parameters for a particular process operation. The parameters may involve selecting two or more sensors from among a plurality of sensors available on the hardware tool. The sensors may be selected because, collectively, they can detect and / or characterize particular events or conditions within the process chamber. Some of these events or conditions are described herein as use cases and applications. In some implementations, block 510 may be performed once during setup and not again thereafter, while other operations in the process are repeatedly performed.

[0088] In some embodiments, the plurality of sensors comprises multiple types of sensors. In some implementations, the plurality of sensors comprises one or more spatial sensors, one or more spectral sensors, and one or more temporal sensors. An example of a spatial sensor may be a camera.

[0089] According to some embodiments, the one or more spatial sensors may include one or more cameras or one or more camera arrays directed toward the interior of the semiconductor manufacturing equipment and configured to obtain image data regarding the interior before, during, and / or after a process. In some embodiments, the one or more spatial sensors may be configured to obtain data regarding characteristics of a plasma present within the interior during a process. Such characteristics of the plasma may include intensity, location, whether it is parasitic, anomalies associated therewith, etc.

[0090] An example of a spectral sensor may be an OES sensor, which may be configured to detect one or more wavelengths of interest emitted by one or more species generated during the process.

[0091] An example of a time sensor may be a photodiode, which may be configured to detect variations in light intensity occurring over periods of less than 1 millisecond, including in the nanosecond and microsecond ranges.

[0092] In some implementations, these spatial, spectral, and temporal sensors may be a set of sensors in which multiple sensor types are combined into one integrated package (or device), which may be incorporated into a viewport (e.g., 900) or other window of semiconductor manufacturing equipment (e.g., multi-sensor fabrication tool 800) and operate according to the description provided below with respect to FIG. 8. While sensor types may be combined into one device, not all may be used. In some scenarios, a set of signals may be provided by all three sensor types. However, in some scenarios, a set of signals may be provided by two sensor types. For example, depending on the application, it may be possible to use only a camera and an OES sensor, or a camera and a photodiode. In some implementations, the multiple sensors may further include other sensor types, such as VI, pressure, or temperature sensors, to provide additional measurements.

[0093] In some implementations, the plurality of sensors may include two of the following types: one or more spatial sensors, one or more spectral sensors, or one or more temporal sensors. For example, in these implementations, the plurality of sensors may be only a camera and an OES sensor, without a photodiode.

[0094] At block 520, the method 500 may include obtaining measurements associated with a set of signals from a selected plurality of sensors. The measurements may be obtained during the process being performed and may be obtained via at least one spatial sensor, at least one spectral sensor, at least one temporal sensor, or combinations thereof, including spatial and spectral, spectral and temporal, spatial and temporal, or at least two types of spatial, spectral, and temporal. The sensors may be used in series or in parallel simultaneously to collect respective signals from the processing environment (e.g., process chamber) and may be primary, secondary, and / or tertiary indicators.

[0095] In some scenarios, a process performed by the semiconductor manufacturing equipment may include detecting an unexpected amount of one or more species within the semiconductor manufacturing equipment, where the type of sensors used to detect the unexpected amount of the species may include at least one spatial sensor and at least one spectral sensor. In some scenarios, a process performed by the semiconductor manufacturing equipment may include detecting an endpoint of a chamber clean within the semiconductor manufacturing equipment, where the type of sensors used to detect the endpoint of the chamber clean may include at least one spatial sensor and at least one spectral sensor. In other scenarios, a process performed by the semiconductor manufacturing equipment may include any one or more of the use cases described elsewhere herein.

[0096] At block 530, method 500 may include determining an indication of the condition of the semiconductor manufacturing equipment based on a combination of data generated from measurements associated with the set of signals (e.g., the measurements from block 520). Advantageously, the combination of data more accurately characterizes the condition of the semiconductor manufacturing equipment than a signal from one of multiple sensors as done in conventional monitoring systems.

[0097] In some embodiments, the method 500 may include modifying control parameters of the semiconductor manufacturing equipment based on the state of the semiconductor manufacturing equipment. Various changes to the control parameters can be made to alter the processing environment, such as the amount or rate of gas species flowing into gas-specific lines, clamping and unclamping the substrate from the pedestal (e.g., through an electrical signal that changes the voltage applied to the electrostatic pedestal), substrate position, chamber clean endpoint, timing of a plasma strike, opening or closing valves, shutting down the manufacturing process to prevent hardware damage (e.g., based on excessive HCD), changing lighting, providing alerts, providing process-related data for manual review (e.g., to adjust the process recipe, equipment design, etc.), and other adjustments to accomplish the numerous use cases described above.

[0098] Alternatively, the state of the semiconductor manufacturing equipment may be ascertained, stored, or provided (eg, displayed, recorded in a computer file, transmitted to another computing device) without changing the control parameters.

[0099] In some embodiments, a machine learning model can be implemented to improve the accuracy of the output. Determining the indicator of the state of the semiconductor manufacturing equipment (block 530) may include using a machine learning model to perform a classification or regression task on at least a portion of the combination of data to obtain measurements associated with the set of signals that the machine learning model is configured to perform. For example, in some cases, a regression model can be implemented using labeled data (e.g., previous measurements and correct labels) to improve the predictability and accuracy of measurements compared to measurements from individual sensors. In other cases, a reduced signal-to-noise ratio from only a single sensor can improve the classification probability (e.g., whether the detected plasma is parasitic, whether there is HF contamination, whether there is film growth) for the primary sensor (e.g., using a logistic model). The utilized machine learning model may be triggered simultaneously throughout the process or at specific intervals depending on the use case.

[0100] FIGS. 6A and 6B are flow diagrams illustrating methods 600 and 650 for determining the status of semiconductor manufacturing equipment, according to some embodiments. One or more of the functions of methods 600 and 650 may be performed by or caused by a computerized device or system. The structure for performing the functionality illustrated in one or more of the steps shown in FIGS. 6A and 6B may include hardware and / or software components of such a computerized device or system, such as, for example, a controller device, a computerized system, or a computer-readable device including a storage medium storing computer-readable and / or computer-executable instructions that, when executed by a processor device, are configured to cause at least one processor device or computerized device to perform an operation. A controller may be an example of a computerized device or system. A subsystem (e.g., 191) may be an example of a computerized device or system. A process chamber may be another example of a computerized device or system. Exemplary components of a process chamber and a controller are shown in FIGS. 1A and 1B, and 13, respectively, and are described in more detail elsewhere herein.

[0101] It should also be noted that the operations of methods 600 and 650 may be performed in any suitable order, not necessarily in the order illustrated in Figures 6A and 6B. Furthermore, methods 600 and 650 may include additional or fewer operations to determine the state of semiconductor manufacturing equipment than those illustrated in Figures 6A and 6B.

[0102] 6A, at block 610, the method 600 may include obtaining a measurement signal via a first sensor. In some embodiments, the first sensor may be a spatial sensor (e.g., a camera), a spectral sensor (e.g., an OES sensor), or a temporal sensor (e.g., a photodiode).

[0103] At block 620, method 600 may include determining whether a trigger condition corresponding to the first sensor is satisfied. The trigger condition may be a condition that triggers different actions, such as collecting information from different sensors, processing sensor signals from one or more sensors in a particular manner, or determining whether to make real-time adjustments to the process. In some examples, the trigger condition is a particular threshold for sensor measurements obtained during a process performed by semiconductor manufacturing equipment. One example may include a measurement value below a predetermined threshold (determined for each sensor type or application or use case), which may indicate uncertainty as to whether the measurement is a valid signal or noise that should be ignored. A particular example described herein may include, for example, plasma conditions (e.g., intensity) within a process chamber during a process performed by semiconductor manufacturing equipment (determined by in situ monitoring with a sensor of the type described throughout this disclosure, e.g., a camera).

[0104] Another example of a trigger condition in a particular scenario may be an intensity shift from an OES sensor, as described elsewhere above. As a further example, the gas composition and its shift (below or above a certain threshold), or the presence of an unexpected species (e.g., oxygen), may be detected by the OES sensor. These measurements may be primary indicators for determining, for example, a gas burst or the endpoint of a chamber clean. Secondary or tertiary confirmation by at least one other type of sensor may be useful to confirm the primary indicators from the aforementioned example sensors (e.g., visual confirmation or additional information from another angle).

[0105] To that end, if the trigger condition is met, the method 600 may proceed to block 630 and include obtaining a measurement signal via a second sensor, which may be a different type of sensor than the first sensor.

[0106] For example, if the first sensor is a spectral sensor, the second sensor may be a spatial or temporal sensor. A spectral sensor (e.g., an OES sensor) may be primarily used to determine the chemical state inside the process chamber, e.g., to determine the progress of chamber cleaning. However, there may be areas where cleaning is slow, or the response from the spectral sensor may be weak, sufficient to determine whether traces of film remain that require cleaning. This response uncertainty may be an example of a trigger condition (block 620). A camera aimed at the location of the traces of film can provide secondary confirmation of the cleaning endpoint. Thus, in this multi-sensor synthesis approach, the OES sensor is the primary indicator in this scenario, and the camera is the secondary indicator for visual confirmation of areas of system chemical uncertainty.

[0107] However, in some embodiments, the first and second sensors may be the same type of sensor. For example, the first sensor may be a camera on one wall of the process chamber facing the top of the pedestal, and the second sensor may be a camera on the opposite wall facing the bottom of the pedestal. In this way, a more complete visual understanding of the conditions inside the process chamber may be obtained.

[0108] Otherwise, if the trigger condition at block 620 is not met, the method 600 may return to block 610 .

[0109] At block 640, the method 600 may include determining whether a trigger condition corresponding to the second sensor is met, where an example of a trigger condition is that the measurement obtained by the second sensor meets or exceeds a predetermined threshold (determined for each sensor type or application or use case), which may indicate the validity of the measurement.

[0110] If the trigger condition is met at block 640, the measurement signal obtained via the second sensor can be used as a secondary confirmation of the measurement signal obtained via the first sensor (e.g., a weak measurement by the first sensor is verified). In some cases, if the second sensor is capable of obtaining a measurement of interest (e.g., plasma intensity can be derived from visual information or spectral measurements), both the measurement signals obtained via the first and second sensors can be used as separate measurements, or the measurement signal obtained via the second sensor can be used as the measurement. If the trigger condition is not met at block 640, method 600 can return to block 610. However, in some implementations, method 600 can return to block 630 and instead obtain an additional measurement signal from the second sensor.

[0111] 6B , in some embodiments, if the trigger condition is not satisfied in block 620, the method may proceed to block 622 and include determining whether a change in the process performed by the semiconductor manufacturing equipment is desirable. In these embodiments, rather than obtaining an additional confirmatory measurement signal via a second sensor (block 630), for example, the trigger may not be satisfied (block 620) and the measurement signal obtained via the first sensor (block 610) still provides sufficient information to conclude that there is an abnormal or undesirable condition in the process chamber or process that requires some modification to be applied to the process to eliminate it, such as a parasitic plasma, a gas leak, a gas composition shift, a gas burst, or an electric arc, so that the process can be modified. In some cases, a determination that a change in the process is desirable may be made based on other factors unrelated to the internal conditions of the process chamber, such as the time spent on the process.

[0112] If a change is desired, in block 624, the method may include changing one or more parameters of the process, and the method may return to block 610 and continue acquiring the measurement signal via the first sensor. Changing the parameters of the process may alter, pause, or stop the process. Depending on various applications, one example of a parameter may include the power state of the process chamber or fabrication tool, and changing the power state may, for example, at least partially shut down the fabrication tool to pause or stop the process. Another example may include changing a gas flow. An unexpected amount of gas or the presence of an unexpected species in the process chamber (e.g., a gas burst or gas leak) may decrease, or an expected gas may increase due to a failure to introduce a sufficient amount of the expected gas. Another example may include the amount of etching performed during chamber cleaning. If a trigger condition indicates, for example, that a large amount of buildup has occurred on the chamber walls, a larger amount of etching may be performed without damaging the chamber walls. Other examples of changing parameters (e.g., temperature, voltage, plasma timing, recipe conditions, other operating parameters) will become apparent from this disclosure.

[0113] If a change is not desired (e.g., if the condition of the process chamber or process may benefit from additional confirmation from a secondary indicator), the method may return from block 622 to block 610 and continue to acquire measurement signals via the first sensor.

[0114] In some embodiments, if the trigger condition is not met at block 640, the method may proceed to block 642 and include determining whether a change in the process performed by the semiconductor manufacturing equipment is desirable, where the determination may be based on measurement signals obtained via the second sensor and / or the first sensor.

[0115] If a change is desired, then in block 644, one or more parameters of the process may be changed, similar to block 624, and the method may return to block 610 to continue acquiring measurement signals via the first sensor. In some embodiments, the method may return to block 630, e.g., to instead acquire measurement signals via a second sensor for further confirmation.

[0116] If a change is not desired, the method may return from block 642 to block 610 and continue to acquire measurement signals via the first sensor. In some embodiments, the method may return to block 630, e.g., to acquire measurement signals via the second sensor instead for further confirmation.

[0117] Methods 600 and 650 represent the sequential collection and / or processing of information from two or more types of sensors, for example, from a process chamber, station, or other portion of a fabrication tool. As described below in the context of Figures 7A and 7B, the collection and / or processing of information can also be performed in parallel and simultaneously.

[0118] 7A and 7B are flow diagrams illustrating methods 700 and 750 for determining the status of semiconductor manufacturing equipment, according to some embodiments. One or more of the functions of methods 700 and 750 may be performed by or caused by a computerized device or system. The structure for performing the functionality illustrated in one or more of the steps shown in FIGS. 7A and 7B may include hardware and / or software components of such a computerized device or system, such as, for example, a controller device, a computerized system, or a computer-readable device including a storage medium storing computer-readable and / or computer-executable instructions that, when executed by a processor device, are configured to cause at least one processor device or computerized device to perform an operation. A controller may be an example of a computerized device or system. A subsystem (e.g., 191) may be an example of a computerized device or system. A process chamber may be another example of a computerized device or system. Exemplary components of a process chamber and a controller are shown in FIGS. 1A and 1B, and 13, respectively, and are described in more detail elsewhere herein.

[0119] It should also be noted that the operations of methods 700 and 750 may be performed in any suitable order, not necessarily in the order illustrated in Figures 7A and 7B. Furthermore, methods 700 and 750 may include additional or fewer operations to determine the state of semiconductor manufacturing equipment than those illustrated in Figures 7A and 7B.

[0120] 7A , at block 710, method 700 may include obtaining measurement signals via a plurality of sensors, which may include at least a first sensor and a second sensor. In some embodiments, the first sensor may be a spatial sensor (e.g., a camera), a spectral sensor (e.g., an OES sensor), or a temporal sensor (e.g., a photodiode). In some embodiments, the second sensor may be a spatial sensor (e.g., a camera), a spectral sensor (e.g., an OES sensor), or a temporal sensor (e.g., a photodiode). In some implementations, the second sensor may be a different type than the first sensor. For example, the first sensor may be a spectral sensor and the second sensor may be a spatial sensor.

[0121] In some embodiments, the measurement signal may be obtained via at least a third sensor. In some embodiments, the first sensor may be a spatial sensor (e.g., a camera), a spectral sensor (e.g., an OES sensor), or a temporal sensor (e.g., a photodiode). In some implementations, the first, second, and third sensors may each be of a different type. For example, the first sensor may be a spectral sensor, the second sensor may be a spatial sensor, and the third sensor may be a temporal sensor. However, in some other embodiments, a combination of different types of sensors may be utilized, for example, two spatial sensors and one spectral sensor.

[0122] In some embodiments, one or more additional sensors may be utilized, which may include sensors capable of obtaining readings of voltage and current (VI), pressure, temperature, etc.

[0123] At block 720, the method 700 may include determining whether trigger conditions corresponding to the plurality of sensors are satisfied. As noted above, the trigger conditions may be conditions that trigger different actions, such as collecting information from different sensors, processing sensor signals from one or more sensors in a particular manner, determining whether to make real-time adjustments to a process, etc.

[0124] If the trigger condition is met at block 720, the method 700 may stop. In some cases, the process chamber or fabrication tool may pause or stop operation in consideration of the trigger condition being met. For example, this may be appropriate if continued operation could damage the tool or the wafer being processed in the tool. In some cases, the process chamber or fabrication tool may proceed to a different process or continue the process.

[0125] If the trigger condition is not met at block 720, the method 700 may return to block 710 to obtain measurement signals via multiple sensors.

[0126] 7B, in some embodiments, if the trigger condition is not met at block 720, the method 700 may include determining whether a change in the process performed by the semiconductor manufacturing equipment is desirable at block 722. An example of determining the desirability of a process change is described above with respect to block 622.

[0127] If a change is desired, then in block 724, the method may include changing one or more parameters of the process, and the method may return to block 710 to continue acquiring measurement signals via the multiple sensors. Example parameters are described above with respect to block 624. By changing the parameters of the process, the process may be altered, paused, or stopped.

[0128] If no change is desired, the method may return from block 722 to block 710 to continue acquiring measurement signals via multiple sensors.

[0129] In these methods, two or more different types of sensors can obtain information collectively from a process chamber, station, or other part of a fabrication tool, rather than continuously (e.g., in response to a trigger condition).

[0130] Device Multi-sensor Capability FIG. 8 is a block diagram illustrating a hardware configuration for a multi-sensor fabrication tool 800 (e.g., multi-station fabrication tool 150 or 182) implementing a combined set (or “suite”) of sensors utilizing multiple types of sensors for fabrication equipment 850, according to some embodiments. In some embodiments, multi-sensor fabrication tool 800 can include a combined set of sensors 810 associated therewith, e.g., located or connected internally or externally to a controller (e.g., system controller 190) or subsystem (e.g., subsystem 191) of multi-sensor fabrication tool 800. Each of the sensors can send or receive a signal from the controller or subsystem, e.g., based on information obtained from fabrication equipment 850. In some embodiments, combined set of sensors 810 can be packaged in a sensor package 810 that interfaces with a viewport or window of a process chamber, such as a process chamber of multi-station fabrication tool 150 or 182. Depending on different implementations, one or more sensor packages may be available per process chamber or station.

[0131] In some implementations, the multi-sensor fabrication tool 800 can include two or more types from multiple sensor types, specifically one or more spatial sensors 812, one or more spectral sensors 814, or one or more temporal sensors 816. These sensor types may be packaged into a set of sensors 810 and not customized for one particular application available for a given sensor type, but the set of sensors 810 can be combined in different ways depending on the implementation. This “omnibus” approach may increase ease of use for multiple applications (and reduce customization), thereby reducing production costs, as well as improve machine availability (MA, the length of time a machine is available to process wafers, for example), improve green-to-green (GtG) period (reduced downtime period, or “red” period) and throughput, improve wafer-to-wafer (WtW) uniformity and wafer quality, and increase precision, stability, and alignment by utilizing different types of sensors in combination that were previously used independently.

[0132] In various embodiments, the sensor package 810 can include one or more physical hardware features that enable the ability for multiple sensors to be used in combination, as described herein. In some implementations, the sensor package 810 can be contained by a housing having at least one major dimension (e.g., major axis, diagonal axis, height, diameter, radius, or other geometry-dependent feature) of about 10 inches (254 mm) or less. Other dimensions may be possible depending on the needs and capabilities of a particular sensor package, which may have the various components described herein. An example of a housing can be housing 1001, described below with respect to FIG. 10. The housing can be shaped to accommodate at least one spatial sensor 812, at least one spectral sensor 814, at least one temporal sensor 816, or, depending on variations, a combination of at least two of the aforementioned types of sensors. In some implementations, the housing can include at least one interface that allows for attachment of the hardware to a wall of a process chamber. The interface can be a physical interface that allows for bonding or coupling, or can involve other mechanisms or structures for attachment to the wall. In some cases, the wall may be part of the heat shield (e.g., 1002, 1003 in FIG. 10 ), or vice versa. In some embodiments, the housing may also include a wired or wireless communication interface and / or a power interface (e.g., a cable), or a structure capable of routing or housing such a communication or power interface. The sensor package 810 may include a structure to secure some or all of the above components, such as a printed circuit board (PCB) or printed wiring board (PWB). In some embodiments, the sensor package 810 may further include an illumination source (e.g., at least one LED), on-board processing capability, a filter (e.g., an optical filter), and / or other components mentioned with respect to FIGS. 9 and 10 below.

[0133] In certain embodiments, two of the aforementioned sensor types may be implemented in the multi-sensor fabrication tool 800. In certain embodiments, all three of the aforementioned sensor types may be implemented in the multi-sensor fabrication tool 800. An example of a spatial sensor 812 may include, but is not limited to, a camera or other image capture device configured to acquire visual or optical information. An example of a spectral sensor 814 may include, but is not limited to, an OES sensor or a spectral reflectometer device. An example of a time sensor 816 may include, but is not limited to, a photodiode or another type of optical sensor. In some embodiments, in addition to the aforementioned sensor types, one or more other sensors (e.g., VI, pressure, temperature) may be included.

[0134] In various embodiments, the set of sensors 810 can be used to obtain spatial, spectral, and / or temporal information that, when analyzed, allows for a better understanding of the state of the system (e.g., observing the process of the multi-sensor fabrication tool 800) and also provides opportunities for control and adjustment or management of process parameters. Two or more types of sensors (including use cases using three types of sensors and / or additional types of sensors, e.g., temperature sensors) can be considered primary sensors or indicators, secondary sensors or indicators, tertiary sensors or indicators, quaternary sensors or indicators, etc.

[0135] In this context, a primary sensor can detect, measure, or otherwise obtain a signal from a process (e.g., from radiation), which can independently determine the presence or change of a process without other sensors (e.g., secondary, tertiary). A secondary sensor can collect data that can assist in obtaining a signal that the primary sensor may not be able to fully or accurately acquire. For example, in some cases, an OES sensor may capture a first portion of the process environment, while a camera may capture a second portion. Using exemplary figures, one sensor may be able to capture 70% of the information, while another sensor may be able to capture the remaining 30% by capturing an additional perspective or type of signal. For example, even if an OES sensor as a primary sensor can detect a film left on the back of a pedestal, a camera may be able to visually detect that a film may be present on the side or top of the pedestal. Without the camera, the OES sensor may not have detected the film on the top of the pedestal. As another example, an OES sensor may not be able to detect the presence of a film due to an insufficient amount of trace material left on the wall, indicating that cleaning is not required for a conventional fabrication tool. However, the camera may detect the film and indicate that the film has been detected. As another example, an OES sensor may detect a sufficient amount of material on a wall but be uncertain about the presence of the film. The camera may, for example, (i) trigger a spatial sensor, such as a camera, to confirm the film's presence only if it determines that the amount of film is detected but insufficient, or (ii) provide a secondary indicator that provides confirmation, working in parallel with the OES sensor to simultaneously provide a powerful composite indicator. In these methods, the primary and secondary sensors may be used together to obtain a more complete picture of the process or its by-products. In this context, a tertiary sensor may be used, for example, as a redundant sensor to act as a backup in case the primary or secondary sensor fails.If the first or second sensors are not working, the tertiary sensor can provide at least partial information. This third sensor may only act as a temporary solution, for example, until the system is taken offline for maintenance. In some cases, all three types of sensors can be used to obtain primary, secondary, and tertiary indicators.

[0136] In some embodiments, measurements from the primary and secondary sensors can be combined to improve the accuracy and / or precision of the measurements or to provide additional relevant information (e.g., using time sensing to detect chamber arcs or spatial or spectral sensing to determine location or source). Tertiary and subsequent sensors may be redundant sensors that are used as a backup in case either the primary or secondary sensors fail, and are not used for measurements. However, in some embodiments, measurement data from tertiary and subsequent sensors can be used in combination with the primary and / or secondary sensors to further improve the accuracy and / or precision of the measurements of interest.

[0137] One particular embodiment of the disclosed system (e.g., multi-sensor fabrication tool 800) combines one or more cameras that provide spatial information in the visible and IR spectrum, an optical emission spectrometer (OES) for spectral detection in the UV, visible, and IR spectrum, a photodiode for rapid time detection of events, and one or more illumination sources to improve detection capabilities. Data obtained from these sensors is combined to generate a stronger signal that enables informed, automated responses in a system that addresses, for example, one of the many use cases described below.

[0138] In some cases, machine learning models or algorithms can be used to improve the accuracy of the output. For example, in some cases, a regression model can be implemented using labeled data (e.g., previous measurements and correct labels) to improve the predictability and accuracy of measurements compared to measurements from individual sensors. In other cases, a reduced signal-to-noise ratio from only a single sensor can improve the classification probability (e.g., whether the detected plasma is parasitic, whether there is HF contamination, whether there is film growth) for the primary sensor (e.g., using a logistic model). The utilized machine learning model may be triggered simultaneously throughout the process or at specific intervals depending on the use case.

[0139] Viewport Components 9 shows a diagram of a cross-sectional view of an exemplary viewport 900 of a fabrication tool (e.g., multi-sensor fabrication tool 800), according to some embodiments, where the viewport 900 has an associated set of sensors (e.g., 810). The set of sensors includes multiple types of sensors, for example, two or more of the spatial, spectral, or temporal sensors mentioned above (or additional sensors such as VI, temperature, pressure, etc.). The viewport 900 may include spatial, spectral, or temporal sensors (three types of sensors) and may be integrated into a process chamber of a station of the fabrication tool.

[0140] In some embodiments, viewport 900 can include a spatial sensor such as camera 901. Camera 901 is mounted to provide optimal visibility of the process chamber and monitor the ongoing process therein. In some implementations, a fabrication tool, a given station, or a process chamber can include multiple cameras, and camera 901 shown in FIG. 9 can be one of the multiple cameras. Similarly, a fabrication tool, a given station, or a process chamber can include multiple viewports, and viewport 900 can be one of the multiple viewports. According to different implementations of the fabrication tool, camera 901 or a viewport having camera 901 can be mounted as shown in FIG. 2 or FIG. 3 or can be mounted in various locations on the chamber wall. Camera 901 can have a cover or shutter that can remain open or closed to protect the lens. In some embodiments, camera 901 can be movable in a certain direction to obtain optical information from different perspectives, such as the face of a showerhead or the surface of a pedestal. The movement is translational in a vertical 910, horizontal 911, or diagonal 912 direction (angle relative to the vertical or horizontal), which directions do not have absolute directional orientations but are relative to each other. In some embodiments, the camera 901 may be tiltable in azimuth and elevation while remaining in the same position relative to the viewport 900.

[0141] In some embodiments, viewport 900 can include a spectral sensor (such as an OES sensor configured to obtain spectral measurements as described herein) and an OES collimator 902 that can narrow a beam of radiation waves emanating from within the process chamber. OES collimator 902 can have a protective cover or shutter that can remain open or closed. In some embodiments, viewport 900 can include a time sensor, such as a photodiode 903 configured to obtain measurements as described herein. In some embodiments, viewport 900 can include an illumination system or one or more illumination sources, such as one or more LEDs 904.

[0142] In some embodiments, the above components may be packaged into a set of sensors (as described with respect to FIG. 8 ) in a viewport 900 of a fabrication tool. Viewport 900 may be positioned relative to other components of the fabrication tool, such as showerhead 905, focus ring 906, pedestal 907, and / or RF liner plate 908.

[0143] FIG. 10 shows a diagram of an external perspective view of a chamber 1000 for an exemplary viewport 900 of a fabrication tool, according to some embodiments. In some embodiments, the chamber 1000 can include a housing 1001, which can include one or more components of FIG. 9 as described above, such as the camera 901, the spectral sensor, the OES collimator 902, the photodiode 903, one or more LEDs 904, or other components described above with respect to the sensor package 810. The housing 1001 can take various shapes (e.g., circular, polygonal (e.g., square), or at least partially conical with angular sides and / or a flat top, as shown in FIG. 10) and can protrude from the exterior, e.g., the exterior of the wall of a process chamber of the fabrication tool. In some implementations, the housing 1001 may take into account material selection for the illumination source and part geometry to avoid reflections onto the spatial sensor. For example, components within the housing 1001 may have a matte or other non-reflective surface to prevent inaccurate illumination of the interior of the fabrication tool. As noted above, the housing may have a major dimension of about 10 inches or less. As noted above, the fabrication tool or its process chamber may include multiple viewports. These multiple viewports can house one or more components, but not necessarily the same components. For example, a first viewport may have a camera but no photodiode, and a second viewport may have a photodiode but no OES sensor. The configuration may depend on whether other views available through the viewports or windows have sufficient fields of view. Multiple such housings 1001 may be provided for use with the fabrication tool or process chamber (e.g., implemented as multiple sensor packages 123 and 125, multiple sensor packages 196, 197, and 198, or multiple sensor packages 394 and 394′).

[0144] In some embodiments, the housing 1001 may include various physical components and structures that support and stabilize the sensor. Depending on the implementation, such structures may include a viewport bezel, LED holder, mounting plate, heat sink, data connector and power cable, printed circuit board (PCB) or printed circuit board assembly (PCBA), lens, and / or outer cover. In some implementations, heat shields or radiation filters (e.g., UV filters) may be included or excluded from some components. Other standard structures, such as rings and fasteners, communication interfaces (e.g., Universal Serial Bus (USB) adapters), wire pass-throughs, fins, reflective surfaces, vents, fans, and coatings, may also be used. Sensors or sensor assemblies (e.g., OES collimator assemblies, photodiodes, cameras) may be positioned, assembled, or otherwise positioned among the aforementioned structures, which may be constructed in various configurations using selected materials (e.g., aluminum), as will be apparent to those skilled in the relevant art.

[0145] In some embodiments, the exterior of the process chamber may include a first heat shield 1002 (e.g., a top heat shield) and / or a second heat shield 1003 (e.g., a bottom heat shield). Data and / or power connectors may be mounted under the first heat shield 1002 or the second heat shield 1003. For example, a fiber connector-physical contact (FC-PC) adapter for the photodiode 903 may be threaded under the heat shield. The heat shields 1002, 1003 may house at least a portion of other components. For example, a viewport may penetrate the second heat shield 1003, as shown in FIG. 10. In other embodiments, the viewport and one or more of its components (e.g., a camera) may be positioned between the first and second heat shields 1002, 1003. Indeed, the viewport may be mounted anywhere appropriate to the fabrication tool or application.

[0146] In some embodiments, to ensure reliable operation, thermal management may be provided by the viewport 900 or the housing 1001 using passive and / or active cooling systems. Other UV, thermal, and RF safety management components and features may also be included in the housing 1001.

[0147] Example Use Cases and Applications There are numerous examples of applications and uses for multiple types of sensors within a process chamber. In some applications, two or more sensors are used, with at least two of the sensors being some combination of spatial, spectral, and temporal sensors. In some applications, one additional such sensor is used in combination with a different type of sensor, such as a current and / or voltage sensor, located within the process chamber (e.g., of fabrication tool 800). Various applications involve using a combination of sensors to detect and / or characterize conditions or events occurring in the process chamber. If such an event or condition is abnormal, a specific response may be required, such as suspending operation, replacing a component, or modifying process conditions. Various examples of conditions and events are provided below.

[0148] One example of such an event is plasma intensity fluctuations within a process chamber. Such fluctuations can have temporal, spectral, and / or spatial characteristics. For example, the fluctuations may have a spike-like or pulse-like temporal structure characterized by duration, tip shape, etc. Such fluctuations can be associated with conditions or events, such as the plasma generator, physical structure, or process gas flow within the process chamber. A temporal sensor such as a photodiode can provide information that allows for fully or partially characterizing the temporal shape of the plasma intensity fluctuations. Such fluctuations may further have a spectral composition associated with a specific gas or other material ionized in the plasma. Information collected by a spectral sensor such as an OES sensor can help elucidate the material associated with the plasma intensity fluctuations. Furthermore, such fluctuations may be located in a specific region or regions within the process chamber. This location can indicate the specific component or components responsible for the plasma intensity fluctuations. To this end, information collected by a spatial sensor such as a camera sensor can enable a systems engineer or process engineer to focus on one or more components associated with the plasma intensity fluctuations.

[0149] A relevant example of an event or condition is the presence of a parasitic plasma in a process chamber. The parasitic plasma is typically in a consistent location, which can be determined with a spatial sensor. It may also have a spectral composition, which can be determined with a spectral sensor. Using information gathered from both the spatial and spectral sensors, the system can determine the location, intensity, and / or ionized gas of the parasitic plasma. This information can be used to replace, clean, or modify affected components and / or adjust process conditions.

[0150] Another example of an event in a process chamber is an RF pulse. As an example, such a pulse can be used to generate a pulsed plasma. RF pulses and associated plasma pulses can have various signatures, such as light emitted at a specific location, light with a specific spectral composition, light with a specific temporal profile, light with a specific voltage, etc. Collectively, this information allows a system to detect and / or characterize RF pulses in a process chamber. This information may be captured using multiple sensors, such as a spatial sensor (e.g., a camera sensor), a spectral sensor (e.g., an OES sensor), a temporal sensor (e.g., a photodiode), a voltage / current sensor, or any combination thereof. In certain embodiments, to detect and / or characterize RF pulses in a process chamber that last above a specific threshold (e.g., 35 ms or longer), the system uses a spatial sensor as a primary indicator, a spectral sensor as a secondary indicator, and a temporal sensor as a tertiary indicator. In some implementations, VI sensors can also be used to acquire voltage and / or current data to supplement the characterization of the RF pulse.

[0151] Another example of an event in a process chamber is a shift in gas composition. This can be due to a variety of causes, some of which are expected and some of which are unexpected. Examples include the introduction of new gas, the failure of an expected gas introduction, and a leak. A shift in gas composition can change certain characteristics of the plasma in the process chamber. Such a change can have spatial and / or spectral characteristics that can be detected by an appropriate sensor. For example, a shift in the intensity of the plasma at the location of the process gas input or the location of a leak can be detected by a spatial sensor such as a camera. Additionally or alternatively, a shift in the spectral composition of the plasma (caused by a shift in gas composition) can be detected by a spectral sensor such as an OES sensor. As an example, a spectral sensor can be used as a primary indicator and a spatial sensor can be used as a secondary indicator to detect and / or characterize a shift in gas composition in a process chamber. In other embodiments, a spatial sensor can be the primary indicator and a spectral sensor can be the secondary indicator.

[0152] Another example of an event in a process chamber is an electrical arc within the chamber. The arc can damage chamber components such as the showerhead or pedestal, introduce defects into the wafer, or drain the system power used to generate the plasma. To detect chamber arcs within a process chamber, a time sensor (e.g., a diode) can be used as a primary indicator (detecting the occurrence of an arc by capturing high-intensity optical emissions), and a spatial sensor (e.g., a camera) can be used as a secondary indicator to locate the arc. In some embodiments, a VI sensor can also be used to acquire voltage and / or current data associated with the electrical arc. In some applications, detecting an arc and its location can facilitate troubleshooting of a defect cluster on a wafer. For example, if defect clusters appear disproportionately on one area of ​​the wafer and sensor information suggests the presence of an arc near the cluster, the defect can be mitigated by addressing the underlying problem causing the arc.

[0153] Chamber Cleaning EndpointAs another example, accurately detecting the chamber cleaning endpoint can be important when operating a process chamber. Processes performed in a process chamber, such as those using a chemical vapor deposition (CVD) process to deposit a conformal material film on a substrate, can deposit a film on the substrate as a by-product of the process, as well as on various chamber surfaces. Over time, the accumulation of unwanted deposits on chamber surfaces can generate particulates and potential contamination, adversely affecting wafer yield. Process chambers are periodically cleaned to remove the accumulation of such by-product particulates. Chamber deposits can be removed, for example, by reacting trace by-product films with a reactive gas (e.g., radical fluorine) to produce silicon tetrafluoride (SiF), which is then removed from the chamber. The optimal cleaning time for a given chamber varies depending on many factors, including the type of material deposited, temperature, pressure, and reactive gas delivery. For example, it is advantageous to determine the chamber cleaning endpoint to prevent over-cleaning due to reaction with the surface itself.

[0154] Chamber cleaning may be performed to maintain the lifespan of the pedestal and / or improve the performance of deposition or other processes (e.g., periodically after precursor chemicals introduced into the process chamber are deposited on the substrate and / or interior surfaces). However, as noted above, trace amounts of chemical by-products can gradually and incrementally accumulate on process chamber components, such as the walls, pedestal, or showerhead. It is desirable to detect a cleaning endpoint and stop the etching process so that the buildup is completely removed but not removed further (without etching the walls or pedestal itself). Current approaches to chamber cleaning involve timed cleaning (which does not account for system or process variability and / or variations in buildup based on different processes) or the use of IR-EPD. IR-EPD looks for a specific voltage and slope of the signal and adds an overetch step. IR-EPD can result in significant etching in some areas of the chamber, potentially shortening the lifespan of the pedestal due to, for example, the formation of AlF3.

[0155] In some embodiments of the present disclosure, a spectral sensor (e.g., an OES sensor) may act as the primary sensor to indicate cleaning status. As cleaning progresses, the OES signal in the light collection area may decrease to the point where, for example, a small film still remaining on the backside of the pedestal does not directly affect the plasma response captured by the OES. If this sensor were used alone, the system may leave a small amount of film behind, which may cause particle problems with repeated cycling. Systematically adding overetching, as in the current approach, may risk shortening the pedestal's lifetime through the formation of excessive reaction byproducts, such as AlF3. However, an OES used in combination with a spatial sensor (e.g., a camera) aimed at the slowest-etching area (e.g., simultaneously with spectral sensing or at a different time relative to spectral sensing) can provide a secondary trigger indicating that cleaning is complete without significant overetching.

[0156] Process engineering (e.g., detection of unexpected gases)As another illustrative use case, consider a gas leak scenario in which an unexpected gas is introduced into a system (e.g., the chamber of a multi-sensor fabrication tool 800) or a larger-than-expected burst of gas occurs during a process step. Process performance checks are typically performed based on sensor response to one of hundreds of correlation channels by visually verifying how the plasma behaves in a particular way when viewed through a viewport, or (most often) through on-wafer performance after the process is complete. Traditional approaches do not provide instantaneous responses. Furthermore, in rapidly changing systems, subtle system drifts may not be easily captured by visual inspection; plasma dynamics are on the order of μs to ms. Constant monitoring of correlation channels is also impractical without an (automated) monitoring system; subtle changes may not be accurately captured by currently used sensors (visual) or may be considered noise in the system. A reliable indicator of process changes is the impact of a particular recipe after processing. Post-process metrology can indicate whether system conditions have changed based on the response of on-wafer properties (thickness, RI, etc.). However, not every wafer is measured. These do not allow for real-time control and can lead to unwanted wafer scrap.

[0157] In some embodiments of the present disclosure, a spatial sensor 812 (e.g., a camera) can be used to monitor the plasma during a process step. A visual model can indicate changes in peak or average intensity within a region of interest. While an instantaneous warning or alert can be generated or displayed by the system, the cause of the impact and corrective action may occur only after the product (e.g., a wafer) is completed and the system status, including gas flow, local pressure, power, etc., is fully determined. This may result in one or more wafer quality issues, and the wafer may be rated as low quality or low yield and / or scrapped entirely. If the camera signal is accompanied by an OES signal indicating that a particular species and its deposition rate may have increased, real-time control of the system may be enhanced. Thus, deposition time or cycle number can be reduced in real time to compensate for excessive deposition rates in a given step (or steps) of the process.

[0158] Further use cases are possible with the multi-sensor fabrication tool 800 using a combination set of sensors 810.

[0159] For example, the combined set of sensors 810 may enable detection of parasitic plasma, where shifts in plasma intensity in two (or more) different areas of the process chamber (e.g., the main cavity, edge, or stem of the pedestal or showerhead) can be monitored using a camera as a primary indicator and an OES sensor as a secondary indicator. In some implementations, this can be done by defining the plasma contour (which may involve additional calculation cycles), Fourier transforming it from the time domain to the frequency domain (>500 Hz associated with ignition failures or particles), determining at what frequencies plasma flicker occurs, and looking for anomalous shifts in intensity that may be associated with the flicker.

[0160] In some implementations, the process chamber and plasma may be monitored for hollow cathode discharge (HCD), for example, in the region of the lift pins that define a storage location for the substrate or wafer. More specifically, in one exemplary process, a camera may be used as a primary indicator to mark the area where the lift pins are located, monitor parasitic plasma in the vicinity (e.g., within a predetermined distance) of the lift pins to indicate an improper location, and capture changes in plasma intensity over time.

[0161] As another example, a camera can be used as a primary indicator to detect hardware deformation. In some cases, showerhead deformation or changes in relative gap profile can be determined based on changes in plasma shape. Additionally, monitoring HCD in the main plasma can prevent hardware damage. In some cases, the number of HCDs can indicate whether to continue the process. For example, if the HCD exceeds 10, the process may be stopped and / or the system may be shut down, and if the HCD is less than 10 (or some other quantity), the system may initiate a "soft" shutdown (e.g., deactivating non-essential modules or processes).

[0162] As another example, film delamination can be detected and prevented by monitoring film growth or delamination in different areas of a process chamber. More specifically, in one exemplary process, a film can be grown in a process chamber using, for example, one of a variety of chemical vapor deposition (CVD) processes, and a camera can be used as a primary indicator to monitor film growth in the chamber or on the wafer.

[0163] As another example, process diagnostics can be performed. A spectral sensor can be used as a primary indicator to measure process gas composition, verify gas connections, evaluate purge timing, and / or perform other associated applications. One exemplary approach to this can be to strike a plasma and then measure the rate of change of a spectral measurement associated with a gas-specific line.

[0164] Another example is detecting air or gas leaks. One exemplary approach to this may involve striking a plasma in the flowing gas, closing all valves, and measuring the change in N2 emissions using a spectral sensor as the primary indicator. In some implementations, the spatial sensor may be spatially isolated, and a combination of the spatial and spectral sensors may be able to detect where and what type of leak has occurred.

[0165] Other example uses and applications of a set of sensors (e.g., in multi-sensor fabrication tool 800) may include plasma spatial variations (primary spatial sensors), gas impurity detection (primary spectral sensors), wafer placement (primary spatial sensors), detection of droplets from a rinse process that allows the substrate to dry (primary spatial sensors), determining plasma health or quality (spectral and spatial sensors), transition consistency between chambers or stations (spectral, spatial, and temporal sensors), alerting a spatial sensor (e.g., a camera) or controller when information changes (e.g., in the process environment) faster than the camera frame rate (primary temporal sensors), determining plasma ignition time when byproducts are not purged effectively, which can lead to ignition delays or changes in the ignition profile (primary temporal sensors), reconstructing plasma uniformity location and quality (spectral and spatial sensors), and other applications and capabilities where the accuracy, strength, or integrity of the detected signal can benefit from multiple sensor types. Using all sensors in combination, it is possible to detect pulses at over 100 samples per second.

[0166] In each of the above use cases and approaches, any type of spatial, spectral, or temporal sensor not explicitly mentioned may be used as a secondary and / or tertiary indicator along with other sensors (e.g., VI, temperature, pressure) and / or illumination sources associated with the processing environment.

[0167] In some cases, the primary and secondary indicators may be reversed. That is, a secondary indicator may be used as a primary indicator (capable of independently or partially obtaining measurements) even if the primary indicator can independently obtain measurements without the secondary indicator. The sensor configuration or packaging for a fabrication tool may not include the desired primary indicator. For example, the set of sensors 810 installed on a fabrication tool may not include a photodiode, but the process may require chamber arc detection suitable for rapid optical detection, in which case the photodiode would be the primary indicator. In such cases, a camera may be able to detect arcs lasting at least in the millisecond range, and thus the camera may instead be the primary indicator.

[0168] FIG. 11 is a flow diagram illustrating a method 1100 for multi-sensor determination of a process chamber cleaning endpoint, according to some embodiments. One or more of the functions of method 1100 may be performed by or caused by a computerized device or system. The structure for performing the functionality illustrated in one or more of the steps shown in FIG. 11 may include hardware and / or software components of such a computerized device or system, such as, for example, a controller device, a computerized system, or a computer-readable device including a storage medium storing computer-readable and / or computer-executable instructions that, when executed by a processor device, are configured to cause at least one processor device or computerized device to perform an operation. A controller may be an example of a computerized device or system. A subsystem (e.g., 191) may be an example of a computerized device or system. A process chamber may be another example of a computerized device or system. Exemplary components of a process chamber and a controller are shown in FIGS. 1A and 1B, respectively, and 13, and are described in more detail elsewhere herein.

[0169] It should also be noted that the operations of method 1100 may be performed in any suitable order, not necessarily in the order depicted in Figure 11. Furthermore, method 1100 may include additional or fewer operations than those depicted in Figure 11 to determine the cleaning endpoint.

[0170] At block 1110, the method 1100 may include initiating a process chamber cleaning process. In some embodiments, the cleaning process may include initiating a remote plasma cleaning and cleaning at least a portion of the process chamber of the fabrication tool for a duration. As known in the relevant art, various approaches can be taken to initiate the cleaning process.

[0171] At block 1120, the method 1100 may include pausing the cleaning process and igniting an inert plasma within the fabrication tool. The pause may be useful to prevent over-etching (or cleaning beyond by-products, e.g., present as a film) of the interior of the process chamber, e.g., walls, components, or other surfaces within the process chamber. Over-etching can cause damage, and determining the least damaging endpoint can advantageously extend the usability and lifespan of the equipment.

[0172] At block 1130, the method 1100 may include collecting spectral information via a spectral sensor (e.g., at least one OES sensor). The spectral sensor may be part of or integrated into a sensor package having multiple types of sensors, including a spectral sensor and at least one other type (e.g., a spatial sensor). In some embodiments, the sensor package used may further include at least another type of sensor (e.g., a temporal sensor). The spectral information may indicate the presence of specific materials and by-products from inside the process chamber. The presence of different trace species may emit different wavelengths that can be detected. Because the surface is made of a different material (e.g., aluminum) than the process by-products, exposure of the surface by cleaning may emit corresponding wavelengths that can be detected.

[0173] At block 1140, method 1100 may include determining whether the spectral information indicates that the cleaning process is complete. In some cases, the spectral information may not change or may only change to an insufficient extent, which may indicate that the chamber cleaning is incomplete because the walls or other internal components have not yet been exposed to the cleaning process. In these cases, further cleaning (e.g., etching) needs to be performed, and method 1100 may return to block 1110. In some cases, the spectral information (e.g., detected wavelengths) may shift, indicating that the surface to be cleaned is exposed rather than covered with a film of previous process by-products. In these cases, method 1100 may proceed to block 1150.

[0174] At block 1150, method 1100 can include collecting spatial information at a particular location. This may be used for secondary confirmation purposes in combination with spectral information as a primary indicator. In some embodiments, the spatial information may be collected via a spatial sensor (e.g., at least one camera). The spatial sensor may be part of or integrated into a sensor package having multiple types of sensors, such as the same sensor package that incorporates a spectral sensor. Both the spectral sensor and the spatial sensor can access the interior of the process chamber using a common viewport or window, as described above with respect to FIGS. 9 and 10. In some embodiments, an illumination source (e.g., an LED) can be activated while capturing the spatial information. For example, the illumination source can illuminate one or more walls of the interior of the process chamber to visually obtain an image of at least the illuminated area.

[0175] At block 1160, method 1100 may include determining whether a particular location is clean. In some embodiments, the spatial information may indicate the presence of trace by-products at the particular location. For example, an image may reveal that an unclean portion of a wall may have a color that is different from the known color of the wall in the absence of by-products or films. In some implementations, the contours or boundaries of the cleaned area or film may be identified, allowing the percentage or amount of clean area and the percentage or amount of area that is not yet clean to be determined or estimated. In some cases, a ratio or percentage of clean area that meets or exceeds a predetermined threshold amount (e.g., 80%) may indicate that a particular location is clean. In some implementations, a transparency level (e.g., based on color content, overall shape, edges, brightness, contrast) that meets a predetermined threshold may indicate that a particular location is clean.

[0176] If the particular location is clean or sufficiently clean (above threshold), method 1100 may end. Otherwise, method 1100 may return to block 1110.

[0177] In some variations, data from the spatial sensors may be captured (block 1150) at the end of spectral data collection (block 1130), regardless of whether the indicators from the spectral information are complete (block 1130). In such cases, the collection of spectral and spatial information may be performed serially or in parallel. This approach, in addition to collecting spectral data, may be used to track cleaning rates in specific areas, which may be used to optimize the cleaning process. The cleaning rate may also further indicate whether a specific location is clean (block 1160).

[0178] In some variations, infrared endpoint detection (IR-EPD) can be used in combination with spectral and spatial information.

[0179] In some variations, spatial information may be collected first as a primary indicator, with spectral information used as secondary confirmatory information.

[0180] 12A and 12B are flow diagrams illustrating methods 1200 and 1250 for determining the presence of unexpected species in a process chamber, according to some embodiments. One or more of the functions of methods 1200 and 1250 may be performed by or caused by a computerized device or system. The structure for performing the functionality illustrated in one or more of the steps shown in FIGS. 12A and 12B may include hardware and / or software components of such a computerized device or system, such as, for example, a controller device, a computerized system, or a computer-readable device including a storage medium storing computer-readable and / or computer-executable instructions that, when executed by a processor device, are configured to cause at least one processor device or computerized device to perform an operation. A controller may be an example of a computerized device or system. A subsystem (e.g., 191) may be an example of a computerized device or system. A process chamber may be another example of a computerized device or system. Exemplary components of a process chamber and a controller are shown in FIGS. 1A and 1B, and 13, respectively, and are described in more detail elsewhere herein.

[0181] It should also be noted that the operations of methods 1200 and 1250 may be performed in any suitable order, not necessarily in the order illustrated in Figures 12A and 12B. Furthermore, methods 1200 and 1250 may include additional or fewer operations than those illustrated in Figures 12A and 12B to determine the presence of unexpected species.

[0182] At block 1210, method 1200 can include collecting spectral and spatial information during a process (e.g., occurring within a process chamber) via a spectral sensor and a spatial sensor, respectively. In some embodiments, the spectral sensor can include at least one OES sensor, and the spatial sensor can include at least one camera, both incorporated into a sensor package and installed on a fabrication tool, e.g., a process chamber. Such a sensor package can access the interior or exterior of the process chamber and can obtain spectral and spatial information through a viewport or window.

[0183] In some variations, only spectral information may be collected, and spatial information may be associated and collected at a later time, e.g., a spatial sensor may be triggered to detect spatial information only if the spectral information indicates the presence of a problem (block 1220).

[0184] At block 1220, method 1200 may include determining whether a problem exists (e.g., the presence of an unexpected gas species). In some embodiments, the determination may be made using a spectral sensor. Consider a scenario in which an expected plasma has a particular spectral signature and / or emission wavelength, e.g., purple. An external leak may occur involving the ingress of air into the process chamber or fabrication system, which may cause the spectral signature and / or emission wavelength to change, e.g., to orange. Such spectral information and / or changes in spectral information may be detected via the spectral sensor.

[0185] If detected, the method 1200 may proceed to block 1230. If not detected, the method 1200 may return to block 1210 and continue collecting spectral and / or spatial information.

[0186] At block 1230, the method 1200 may include using a spatial sensor to determine whether a gas leak associated with the unexpected gas species has been located. The spatial sensor, e.g., a camera, may visually isolate the problem (e.g., gas leak) identified by the spectral sensor. Gas leaks and the presence of unexpected gases may occur outside or inside the process chamber. Visually isolating the gas leak may enable an end user or personnel to notice the leak and / or perform root cause corrective action (RCCA) to prevent the defect from recurring by addressing its cause.

[0187] If located, method 1200 terminates at block 1290. In some embodiments, termination may occur after or simultaneously with the RCCA. For example, the fabrication tool or a portion thereof (e.g., a process chamber) may be placed in a shutdown mode or at least partially disabled (e.g., certain gas lines).

[0188] If not, the method 1200 may return to block 1210 and continue to acquire measurement signals via the spectral and spatial sensors. In some implementations, the method 1200 may return to block 1220 or repeat block 1230 to attempt to locate the gas leak.

[0189] However, referring now to FIG. 12B, in some implementations, if located at block 1230, rather than terminating the process (block 1290), the method 1200 may proceed to block 1240, which may include determining whether to terminate the process.

[0190] If it is determined that the process should not be terminated, the method 1200 may proceed to block 1242, which may include determining whether to modify process parameters. Modifying process parameters may have a beneficial effect on the process, such as improving product yield or process savings.

[0191] If it is determined that changing the process parameters does not have or is unlikely to have a beneficial effect, the method 1200 may return to block 1210. In some variations, the method may end at block 1290.

[0192] If it is determined that changing the process parameters will have or are likely to have a beneficial effect, the method 1200 may proceed to block 1244, which may include changing one or more parameters of the process, and the method may return to block 1210 to continue acquiring measurement signals via the spectral and spatial sensors. Examples of process parameters are described elsewhere herein (e.g., power conditions, gas reduction or increase, plasma timing, recipe conditions).

[0193] Calculation and Controller Embodiments 13 is a block diagram of an example computing device 1300 suitable for use in practicing some embodiments of the present disclosure. For example, device 1300 may be suitable for implementing some or all of the functionality of the image analysis logic disclosed herein.

[0194] Computing device 1300 may include a bus 1302 that directly or indirectly couples the following devices: memory 1304, one or more central processing units (CPUs) 1306, one or more graphics processing units (GPUs) 1308, a communications interface 1310, input / output (I / O) ports 1312, input / output components 1314, a power supply 1316, and one or more presentation components 1318 (e.g., displays). In addition to CPU 1306 and GPU 1308, computing device 1300 may include additional logic devices not shown in FIG. 13, such as, but not limited to, an image signal processor (ISP), a digital signal processor (DSP), an ASIC, an FPGA, etc.

[0195] While the various blocks in FIG. 13 are shown connected by lines via bus 1302, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component 1318, such as a display device, may be considered an I / O component 1314 (e.g., if the display is a touchscreen). As another example, CPU 1306 and / or GPU 1308 may include memory (e.g., memory 1304 may represent a storage device in addition to the memory of GPU 1308, CPU 1306, and / or other components). In other words, the computing devices of FIG. 13 are merely illustrative. Categories such as “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “handheld device,” “electronic control unit (ECU),” “virtual reality system,” and / or other device or system types are not distinguished and are all considered within the scope of the computing devices of FIG. 13.

[0196] Bus 1302 may represent one or more buses, such as an address bus, a data bus, a control bus, or a combination thereof. Bus 1302 may include one or more bus types, such as an Industry Standard Architecture (ISA) bus, an Extended Industry Standard Architecture (EISA) bus, a Video Electronics Standards Association (VESA) bus, a Peripheral Component Interconnect (PCI) bus, a Peripheral Component Interconnect Express (PCIe) bus, and / or another type of bus.

[0197] The memory 1304 may include any of a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by the computing device 1300. Computer-readable media may include both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, computer-readable media may include computer storage media and / or communication media.

[0198] Computer storage media may include both volatile and nonvolatile media, and / or removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 1304 may store computer-readable instructions (e.g., representing programs and / or program elements, such as an operating system). Computer storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by computing device 1300. As used herein, computer storage media does not include the signals themselves.

[0199] Communication media may embodi computer-readable instructions, data structures, program modules, and / or other data types in a modulated data signal such as a carrier wave or other transport mechanism and include any information delivery media. The term "modulated data signal" may refer to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.

[0200] The CPU 1306 may be configured to execute computer-readable instructions to control one or more components of the computing device 1300 and to implement one or more of the methods and / or processes described herein. The CPUs 1306 may each include one or more cores (e.g., 1, 2, 4, 8, 28, 72, etc.) capable of simultaneously processing multiple software threads. The CPUs 1306 may include any type of processor and may include different types of processors depending on the type of computing device 1300 implemented (e.g., a processor with fewer cores for mobile devices and a processor with more cores for servers). For example, depending on the type of computing device 1300, the processor may be an ARM processor implemented using reduced instruction set computing (RISC) or an x86 processor implemented using complex instruction set computing (CISC). The computing device 1300 may include one or more CPUs 1306 in addition to one or more microprocessors or auxiliary coprocessors, such as math coprocessors.

[0201] The GPU 1308 may be used by the computing device 1300 to render graphics (e.g., 3D graphics). The GPU 1308 may include many (e.g., tens, hundreds, or thousands) of cores capable of processing many software threads simultaneously. The GPU 1308 may generate pixel data for an output image in response to a rendering command (e.g., a rendering command from the CPU 1306 received via a host interface). The GPU 1308 may include graphics memory, such as a display memory, for storing pixel data. The display memory may be included as part of the memory 1304. The GPU 1308 may include two or more GPUs operating in parallel (e.g., via a link). In combination, each GPU 1308 may generate pixel data for a different portion of an output image or for different output images (e.g., one GPU for a first image and a second GPU for a second image). Each GPU may include its own memory or may share memory with other GPUs.

[0202] In examples where the computing device 1300 does not include a GPU 1308, the CPU 1306 may be used to render graphics.

[0203] The communications interface 1310 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 1300 to communicate with other computing devices over electronic communications networks, including wired and / or wireless communications. The communications interface 1310 may include components and functionality that enable communication over any of a number of different networks, such as a wireless network (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), a wired network (e.g., communication over Ethernet), a low-power wide area network (e.g., LoRaWAN, SigFox, etc.), and / or the Internet.

[0204] The I / O ports 1312 may enable the computing device 1300 to be logically coupled to other devices, including I / O components 1314, presentation components 1318, and / or other components, some of which may be built-in (e.g., integrated) to the computing device 1300. Exemplary I / O components 1314 include a microphone, mouse, keyboard, joystick, trackpad, satellite dish, scanner, printer, wireless device, etc. The I / O components 1314 may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological input generated by a user. In some cases, the input may be transmitted to an appropriate network element for further processing. The NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric authentication, on-screen and near-screen gesture recognition, air gestures, head and eye tracking, and touch recognition associated with the display of the computing device 1300 (described in more detail below). The computing device 1300 may include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations thereof, for gesture detection and recognition. Additionally, the computing device 1300 may include an accelerometer or gyroscope (e.g., as part of an inertial measurement unit (IMU)) that enables detection of movement. In some examples, the output of the accelerometer or gyroscope may be used by the computing device 1300 to render immersive augmented or virtual reality.

[0205] The power source 1316 may include a wired power source, a battery power source, or a combination thereof. The power source 1316 may provide power to the computing device 1300, enabling the components of the computing device 1300 to operate.

[0206] The presentation component 1318 may include a display (e.g., a monitor, a touch screen, a television screen, a heads-up display (HUD), other display types, or a combination thereof), speakers, and / or other presentation components. The presentation component 1318 can receive data from other components (e.g., the GPU 1308, the CPU 1306, etc.) and output that data (e.g., as images, video, sound, etc.).

[0207] The present disclosure may be described in the general context of computer code or machine-usable instructions, including computer-executable instructions, such as program modules, executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules, including routines, programs, objects, components, data structures, etc., refer to code that performs particular tasks or implements particular abstract data types. The present disclosure may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.

[0208] In some embodiments, the "controller" (e.g., 190) is part of a system that includes various types of sensors as described herein. Such systems include fabrication tools equipped with camera sensors. Such systems may include semiconductor processing equipment, including one or more processing tools, one or more chambers, one or more processing platforms, and / or specific processing components (e.g., wafer pedestals, gas flow systems, etc.). These systems may be integrated with electronics for controlling system operation before, during, and after processing of semiconductor wafers or substrates. The controller may be implemented with or coupled to the analytical logic described above. The controller may be implemented as logic, such as electronics with one or more integrated circuits, memory devices, and / or software, that receives instructions, issues instructions, controls operations, and / or enables sensing operations.

[0209] The electronics may be referred to as a "controller" and may control various components or subcomponents of one or more systems. The controller may be programmed to control any of the processes disclosed herein depending on the processing requirements and / or type of system. Such processes may include process gas delivery, temperature settings (e.g., heating and / or cooling), pressure settings, vacuum settings, power settings, radio frequency (RF) generator settings in some systems, RF matching circuit settings, frequency settings, flow rate settings, fluid delivery settings, position and motion settings, wafer transfer to and from tools and other transfer tools connected to or interfaced with a particular system, and / or wafer transfer to and from load locks.

[0210] Broadly, a controller may be defined as an electronic device having various integrated circuits, logic, memory, and / or software that receive instructions, issue instructions, control operations, enable cleaning operations, enable endpoint measurements, etc. Integrated circuits may include chips in the form of firmware that store program instructions, digital signal processors (DSPs), chips defined as application-specific integrated circuits (ASICs), and / or one or more microprocessors, i.e., microcontrollers, that execute program instructions (e.g., software). Program instructions may be instructions communicated to the controller in the form of various individual settings (or program files) that define operational parameters for performing a particular process on or for a semiconductor wafer or for a system. The operational parameters, in some embodiments, may be part of a recipe defined by a process engineer to accomplish one or more processing steps in the processing of one or more layers, materials, metals, oxides, silicon, silicon dioxide, surfaces, circuits, and / or wafer dies.

[0211] The controller can be configured to control or cause the control of various components or subcomponents of one or more systems. The controller may be programmed to control any of the processes that may be used by the fabrication tool during fabrication operations, depending on the processing requirements and / or the type of system. Such processes include adjusting or maintaining process gas delivery, temperature settings (e.g., heating and / or cooling), including substrate temperature and chamber wall temperature, pressure settings, including vacuum settings, plasma settings, RF matching circuit settings, and substrate position and operation settings, including substrate transfer to and from the fabrication tool and / or load lock. Process gas parameters include process gas composition, flow rate, temperature, and / or pressure. Of particular relevance to the disclosed embodiments, controller parameters may relate to plasma generator power, pulse rate, and / or RF frequency.

[0212] The process parameters under the control of the controller may be provided in the form of a recipe and entered using a user interface. Signals for monitoring the process may be provided by analog and / or digital input connections of the system controller. Signals for controlling the process may be output at analog and digital output connections of the deposition tool.

[0213] In one example, instructions for igniting or maintaining a plasma are provided in the form of a process recipe. Associated process recipes may be arranged sequentially, allowing at least some instructions for the process to be executed simultaneously. In some implementations, instructions for setting one or more plasma parameters may be included in a recipe preceding a plasma ignition process. For example, a first recipe may include instructions for a first time delay, instructions for setting a flow rate of an inert gas (e.g., helium) and / or a reactant gas, and instructions for setting a plasma generator to a first power setpoint. A second, subsequent recipe may include instructions for a second time delay and instructions for enabling the plasma generator to provide power under a defined set of parameters. A third recipe may include instructions for a third time delay and instructions for disabling the plasma generator. It will be understood that these recipes may be further subdivided and / or repeated in any suitable manner within the scope of the present disclosure. In some deposition processes, the duration of plasma strike may correspond to a duration of several seconds, such as from about 3 seconds to about 15 seconds, or may involve longer durations, such as up to about 30 seconds. In certain embodiments described herein, much shorter plasma strikes may be applied during a treatment cycle. Such plasma strike durations may be on the order of less than about 50 milliseconds, with specific examples utilizing about 25 milliseconds. As described, the plasma may be pulsed.

[0214] In some embodiments, the controller is configured to control and / or manage operation of the RF signal generator. In certain implementations, the controller is configured to determine upper and / or lower thresholds for RF signal power delivered to the fabrication tool, determine the actual (e.g., real-time) level of RF signal power delivered to the integrated circuit fabrication chamber, RF signal power on / off times, RF signal on / off durations, duty cycle, operating frequency, etc.

[0215] As a further example, the controller may be configured to control the timing of various operations, the mixture of gases, the pressure within the fabrication tool, the temperature within the fabrication tool, the temperature of the substrate or pedestal, the position of the pedestal, chuck, and / or susceptor, and the number of cycles performed on one or more substrates.

[0216] The controller may include one or more programs or routines for controlling designed subsystems associated with the fabrication tool. Examples of such programs or routines include a substrate positioning program, a process gas control program, a pressure control program, a heater control program, and a plasma control program. The substrate positioning program may include program code for process tool components used to load the substrate onto the pedestal and control the spacing between the substrate and other parts of the fabrication tool. The positioning program may include instructions for moving the substrate into and out of the reaction chamber for depositing a film on the substrate and cleaning the chamber.

[0217] The process gas control program may include code for controlling gas composition and flow rates and code for flowing gases to one or more process stations prior to deposition to stabilize the pressure within the process stations. In some embodiments, the process gas control program includes instructions for introducing gases during film formation on substrates within the reaction chamber. This may include introducing gases for different numbers of cycles for one or more substrates within a batch of substrates. The pressure control program may include code for controlling the pressure within the process station, for example, by adjusting a throttle valve in the exhaust system of the process station, gas flow to the process station, etc. The pressure control program may include instructions for maintaining the same pressure during different numbers of cycles of deposition on one or more substrates during processing of a batch.

[0218] The heater control program may include code for controlling the current to a heating unit used to heat the substrate, or the heater control program may control the delivery of a heat transfer gas (such as helium) to the substrate.

[0219] In some embodiments, there may be a user interface associated with the controller, which may include a display screen, a graphical software display of equipment and / or process conditions, and user input devices such as a pointing device, keyboard, touch screen, microphone, etc.

[0220] In some embodiments, the controller may be part of, coupled to, or a combination of a computer integrated with, coupled to, or otherwise networked to the system. For example, the controller may be in the “cloud” or all or part of a fab host computer system. This allows for remote access to wafer processing. The computer may provide remote access to the system to monitor the current progress of processing operations, review the history of past processing operations, review trends or performance criteria from multiple processing operations, modify parameters of a current process, configure processing steps following a current process, or initiate a new process. In some examples, a remote computer (e.g., a server) can provide process recipes to the system over a network. Such a network may include a local network or the Internet. The remote computer may include a user interface that allows entry or programming of parameters and / or settings, which are then communicated to the system from the remote computer. In some examples, the controller receives instructions in the form of data. Such data identifies parameters for each processing step performed during one or more operations. It should be understood that the parameters may be specific to the type of process being performed and the type of tool the controller is configured to interface with or control. Thus, as discussed above, the controller may be distributed, for example, by having one or more individual controllers networked together and working together toward a common purpose (such as the processes and controls described herein). An example of a distributed controller for such purposes would include one or more integrated circuits on the chamber in communication with one or more integrated circuits located remotely (e.g., at the platform level or as part of a remote computer) and coupled to control the process in the chamber.

[0221] Exemplary systems may include, but are not limited to, a plasma etch chamber or module, a deposition chamber or module, a spin rinse chamber or module, a metal plating chamber or module, a cleaning chamber or module, a bevel edge etch chamber or module, a physical vapor deposition (PVD) chamber or module, a chemical vapor deposition (CVD) chamber or module, an atomic layer deposition (ALD) chamber or module, an atomic layer etch (ALE) chamber or module, an ion implantation chamber or module, a tracking chamber or module, and any other semiconductor processing system that may be associated with or used in the processing and / or manufacturing of semiconductor wafers.

[0222] The system software can be configured in many different ways, possibly with different architectures. For example, various chamber component subroutines or control objects may be written to control the operation of the chamber components necessary to perform the deposition process (and possibly other processes) in accordance with the disclosed embodiments.

[0223] As noted above, depending on the process step or steps being performed by the tool, the controller may communicate with one or more other tool circuits or modules, other tool components, cluster tools, other tool interfaces, adjacent tools, nearby tools, tools located throughout the factory, a main computer, another controller, or tools used in material transport to and from tool locations and / or load ports in a semiconductor fabrication factory.

[0224] Various modifications to the embodiments described in this disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the claims are not intended to be limited to the embodiments shown herein but are to be accorded the widest scope consistent with this disclosure, the principles and novel features disclosed herein.

[0225] Certain features that are described herein in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Furthermore, while features may be described above as acting in a particular combination and may even be initially claimed as such, one or more features from a claimed combination may, in some cases, be excluded from the combination, and the claimed combination may be directed to a subcombination or variation of the subcombination.

[0226] Similarly, although operations are depicted in a particular order in the figures, this should not be understood as requiring that such operations be performed in the particular order depicted or in any sequential order, or that all depicted operations be performed, to achieve desirable results. Furthermore, the figures may also generally illustrate an exemplary process in the form of a flow diagram. However, other operations not depicted may be incorporated into the generally depicted exemplary process. For example, one or more additional operations may be performed before, after, simultaneously with, or between any of the depicted operations. Multitasking and parallel processing may be advantageous in certain situations. Furthermore, the separation of various system components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated into a single software product or packaged into multiple software products. Additionally, other embodiments are within the scope of the following claims. In some cases, the operations recited in the claims may be performed in a different order and still achieve desirable results.

Claims

1. Semiconductor manufacturing equipment, a plurality of sensors disposed around the semiconductor manufacturing equipment, the plurality of sensors comprising one or more spatial sensors, one or more spectral sensors, and one or more temporal sensors; a controller communicatively coupled to the plurality of sensors, determining a set of signals from the plurality of sensors to monitor during a process performed by the semiconductor manufacturing equipment; During the process, measurements associated with the set of signals from the plurality of sensors are obtained; determining an indication of a condition of the semiconductor manufacturing equipment based on a combination of data generated from the measurements associated with the set of signals; and a controller that is configured to run A manufacturing system comprising:

2. 10. The manufacturing system of claim 1, the set of signals is provided by at least two sensor types, the at least two sensor types comprising at least two of: (i) at least one of the one or more spatial sensors, (ii) at least one of the one or more spectral sensors, or (iii) at least one of the one or more temporal sensors; at least one of the one or more spatial sensors comprises a camera directed toward the interior of the semiconductor manufacturing equipment; at least one of the one or more spectral sensors comprises an optical emission spectroscopy (OES) sensor configured to detect one or more wavelengths of interest emitted by one or more species produced during the process; and at least one of the one or more temporal sensors comprises a photodiode configured to detect variations in light intensity occurring over a period of about 1 millisecond or less. Manufacturing system.

3. 3. The manufacturing system of claim 2, The process performed includes detecting unexpected amounts of one or more species within the semiconductor manufacturing equipment; the at least two sensor types comprise the one or more spatial sensors and the one or more spectral sensors; Manufacturing system.

4. 3. The manufacturing system of claim 2, The process performed includes endpoint detection of a chamber clean within the semiconductor manufacturing equipment; the at least two sensor types used simultaneously comprise the one or more spatial sensors and the one or more spectral sensors; Manufacturing system.

5. 10. The manufacturing system of claim 1, The manufacturing system, wherein the controller is further configured to modify a control parameter of the semiconductor manufacturing equipment based on the state of the semiconductor manufacturing equipment.

6. 10. The manufacturing system of claim 1, The combination of the data more accurately characterizes the condition of the semiconductor manufacturing equipment than a signal from one of the plurality of sensors.

7. 10. The manufacturing system of claim 1, the controller comprises a machine learning model; and obtaining the measurements associated with the set of signals configured to perform a classification or regression task on at least a portion of the data combinations using the machine learning model. Manufacturing system.

8. 10. The manufacturing system of claim 1, the one or more spatial sensors comprise one or more cameras or one or more camera arrays directed toward an interior of the semiconductor manufacturing equipment and configured to obtain image data regarding the interior before, during, and / or after the process.

9. 9. The manufacturing system of claim 8, further comprising one or more illumination sources configured to provide illumination to the interior of the semiconductor manufacturing equipment; the illumination enables the one or more cameras or the one or more camera arrays to acquire the image data regarding the interior before, during, and / or after the process; Manufacturing system.

10. 10. The manufacturing system of claim 1, the one or more spatial sensors comprise one or more cameras or one or more camera arrays directed toward an interior of the semiconductor manufacturing equipment and configured to obtain data regarding one or more characteristics of a plasma present therein during the process, the one or more characteristics of the plasma including one or more of an intensity, a boundary, or a location of the plasma.

11. 10. The manufacturing system of claim 1, 10. The semiconductor manufacturing system of claim 1, further comprising: one or more illumination sources configured to provide one or more pulses of light to at least a portion of an interior of the semiconductor manufacturing equipment, the one or more pulses of light being asynchronous with one or more operations of the one or more spatial sensors.

12. 1. A method for multi-sensor determination of a state of semiconductor equipment, comprising: determining a set of signals from a plurality of sensors of the semiconductor equipment monitored during a process performed by the semiconductor equipment, the plurality of sensors comprising at least one spatial sensor, at least one spectral sensor, and at least one temporal sensor; Based on the process being performed, performing sensor measurements via two or more of: (i) the at least one spatial sensor, (ii) the at least one spectral sensor, or (iii) the at least one temporal sensor; determining the state of the semiconductor equipment based on the sensor measurements; A method comprising:

13. 13. The method of claim 12, The method, wherein the at least one spatial sensor comprises a camera, the at least one spectral sensor comprises an optical emission spectroscopy (OES) sensor, and the at least one temporal sensor comprises a photodiode.

14. 13. The method of claim 12, The process performed comprises detecting an unexpected amount of one or more species within the semiconductor device; two or more of (i) to (iii) comprise the at least one spatial sensor and the at least one spectral sensor; method.

15. 13. The method of claim 12, the process being performed comprises detecting an endpoint of a chamber clean within the semiconductor equipment; two or more of (i) to (iii) comprise the at least one spatial sensor and the at least one spectral sensor; method.

16. 13. The method of claim 12, further comprising: modifying a control parameter of the semiconductor equipment based on the state of the semiconductor equipment.

17. 13. The method of claim 12, further comprising:

12. A method comprising: using one or more illumination sources of the semiconductor device to verify functionality of one or more of: (i) the at least one spatial sensor, (ii) the at least one spectral sensor, or (iii) the at least one temporal sensor.

18. 13. The method of claim 12, further comprising:

11. A method comprising: providing pulsed light to at least a portion of an interior of the semiconductor device in a pulsed manner using one or more illumination sources, the pulsed light being asynchronous with operation of the at least one spatial sensor.

19. 1. A multi-sensor measurement device having access to the interior of a fabrication tool, comprising: a housing having a major dimension of about 10 inches (254 mm) or less; a spectral sensor within the housing; a spatial sensor within the housing; a time sensor within the housing; a physical interface, the housing attached to a surface of the fabrication tool and shaped to allow reception of electromagnetic signals associated with a process performed by the fabrication tool through a window in the fabrication tool; A multi-sensor measurement device comprising:

20. 20. A multi-sensor measurement device according to claim 19, The fabrication tool includes a process chamber having one or more stations.

21. 20. A multi-sensor measurement device according to claim 19, the spatial sensor comprises an optical emission spectroscopy (OES) sensor configured to acquire spectral information; the spectral sensor comprises a camera configured to acquire visual information; the time sensor comprises a photodiode configured to acquire time information; Multi-sensor measuring device.

22. 22. A multi-sensor measurement device according to claim 21, The multi-sensor measurement device further comprises a communication interface configured to transmit measurements related to at least two of the spectral information, the visual information, the temporal information, or a combination thereof.

23. 23. A multi-sensor measurement device according to claim 22, the process performed by the fabrication tool includes detecting an unexpected amount of one or more species within the fabrication tool; the measurements are associated with the spectral information and the visual information; Multi-sensor measuring device.

24. 23. A multi-sensor measurement device according to claim 22, the process performed by the fabrication tool includes endpoint detection of a chamber clean of the interior of the fabrication tool; the measurements are associated with the spectral information and the visual information; Multi-sensor measuring device.

25. 20. A multi-sensor measurement device according to claim 19, further comprising an illumination source within the housing, the illumination source configured to provide illumination to at least a portion of an interior of the production tool; the illumination enables the spatial sensor to obtain visual information regarding the at least a portion of the interior of the fabrication tool before, during, and / or after the process performed by the fabrication tool, and includes pulses of light asynchronous with obtaining visual information regarding the at least a portion of the interior of the fabrication tool, facilitating calibration or validation of one or more of the spectral sensor, the spatial sensor, or the temporal sensor, or a combination thereof; Multi-sensor measuring device.

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