Spectral sensing of processing chamber conditions

Abstract

Methods, apparatus, and various use applications are provided for spectrally sensing conditions of a processing chamber of a semiconductor device manufacturing apparatus.

Description

Related Applications

[0001] The PCT application form is filed simultaneously with this specification as part of this application. Each application identified in the PCT application form filed simultaneously that this application claims the benefit of or priority to is hereby incorporated by reference in its entirety and for all purposes. Background Art

[0002] Sensors associated with semiconductor device manufacturing equipment, such as multi-station manufacturing tools (including, for example, multi-station processing chambers), include sensors for specific tasks or classes of problems. Thus, the sensors can detect and collect specific types of useful information. For example, optical emission spectroscopy (OES) measures the spectral content of electromagnetic radiation, such as light emitted in the aforementioned processing chamber. Such light may be caused by a chemical reaction (e.g., chemiluminescence) or the excitation of a gaseous species by a plasma introduced into the processing chamber.

[0003] The background and overview contained herein are provided only for the purpose of presenting the content of the present disclosure in a general manner. Most of the present disclosure presents the work of the inventors, and just because such work is described in the background art section or presented as content elsewhere herein does not mean that it is admitted to be prior art. Summary of the Invention

[0004] In one aspect of the present disclosure, a semiconductor device manufacturing apparatus is disclosed. In some embodiments, the semiconductor device manufacturing apparatus includes: a processing chamber; at least one sensor having access to the processing chamber; and a controller communicatively coupled to the at least one sensor, the controller being configured to: (a) introduce a chamber cleaning substance into the processing chamber to remove a coating from one or more components of the processing chamber without exciting or generating a plasma in the processing chamber; (b) during chamber cleaning, use the at least one sensor to detect spectral characteristics of electromagnetic radiation emitted in the processing chamber; and (c) determine based on the spectral characteristics that at least a portion of the electromagnetic radiation emitted in the processing chamber is caused by a chemical reaction of the chamber cleaning substance with at least one of the coating or one or more components of the processing chamber.

[0005] In another aspect of the present disclosure, a method for detecting a cleaning endpoint is disclosed. In some embodiments, the method includes: (a) introducing a chamber cleaning substance into a processing chamber to remove a coating from one or more components of the processing chamber without exciting or generating a plasma in the processing chamber; (b) generating a reference plasma; (c) detecting spectral characteristics of electromagnetic radiation emitted by one or more substances excited by the reference plasma in the processing chamber; and (d) determining, based on the spectral characteristics, that the coating has been removed from at least one of the one or more components of the processing chamber.

[0006] In another aspect of the present disclosure, a semiconductor manufacturing apparatus is disclosed. In some embodiments, the semiconductor manufacturing apparatus includes: a processing chamber; at least one sensor having access to the processing chamber; and a controller communicatively coupled to the at least one sensor, the controller being configured to: (a) expose the processing chamber to fluorine and / or a fluorine-containing substance; (b) sweep the processing chamber one or more times; (c) generate a reference plasma in the processing chamber; (d) detect spectral characteristics of electromagnetic radiation emitted by a substance excited by the reference plasma in the processing chamber; (e) determine, based on the spectral characteristics, that no harmful substances are present in the processing chamber; and (f) based on determining that no harmful substances are present in the processing chamber, not perform further sweeping of the processing chamber.

[0007] In another aspect of the present disclosure, a device for monitoring and controlling semiconductor device manufacturing equipment is disclosed. In some embodiments, the device includes: at least one spectral sensor; and a controller communicatively coupled to the at least one sensor, the controller being configured to: detect spectral characteristics of emissions from an interior portion of the semiconductor device manufacturing equipment using the at least one spectral sensor; and adjust a process related to the semiconductor device manufacturing equipment toward a desired process condition in response to determining, based on the detected spectral characteristics, that the desired process condition within the interior portion has not been achieved.

[0008] In another aspect of the present disclosure, a method for detecting a limited spectral signal in a processing chamber of semiconductor device manufacturing equipment is disclosed. In some embodiments, the method includes: (a) generating a reference plasma in a processing chamber that contains a first chemical substance used in a plasma-less process; (b) detecting spectral characteristics of light emitted by one or more substances excited by the reference plasma in the processing chamber; and (c) determining, based on the spectral characteristics, that the first chemical substance is present in the processing chamber.

[0009] In another aspect of the present disclosure, a method for indirectly determining the chamber state of a processing chamber is disclosed. In some embodiments, the method includes: (a) generating a plasma in a processing chamber comprising a first chamber state, wherein the first chamber state affects the plasma in a manner such that the plasma exhibits a first plasma state; (b) measuring a value of an optical property at a first spectral feature of a substance in the processing chamber, wherein the first spectral feature is sensitive to the first plasma state; and (c) determining, based on the value of the optical property, that the first chamber state is present in the processing chamber.

[0010] These and other features of the disclosed embodiments will be described in detail below with reference to the related drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1A A fabrication tool for depositing or etching a film on or above a substrate using plasma processing; the tool includes a spectral sensor.

[0012] Figure 1B A schematic diagram depicting an implementation of a multi-station processing tool; the tool includes four camera sensors.

[0013] Figure 1C A top view of an electronic device manufacturing system presenting four multi-station fabrication tools, one of the four multi-station fabrication tools including a camera sensor.

[0014] Figure 2A Schematically depicts an implementation of such a spectral sensor having a processing chamber or station.

[0015] Figure 2B Is a diagram of an exemplary spectral sensor according to some embodiments.

[0016] Figure 3 Depicts an exemplary spectrum obtained using spectral sensing, which depicts a spectral signal obtained as a function of wavelength over time.

[0017] Figure 4A and Figure 4B Are flowcharts depicting a method for monitoring and controlling semiconductor device manufacturing equipment according to some embodiments.

[0018] Figure 5 Displays a graph comparing signals obtained via spectral sensing and infrared-based signals.

[0019] Figure 6 Is a flowchart showing a method for detecting a cleaning endpoint according to some embodiments.

[0020] Figure 7is a flowchart depicting another method for detecting a cleaning endpoint according to some embodiments.

[0021] Figure 8A is an exemplary spectrogram that indicates peaks corresponding to contamination caused by harmful substances during multiple cleaning cycles.

[0022] Figure 8B is related to Figure 8A an exemplary graph that indicates signals related to contamination during multiple cleaning cycles.

[0023] Figure 9 is a flowchart depicting another method for determining an optimal number of cleaning cycles for a semiconductor device manufacturing apparatus according to some embodiments.

[0024] Figure 10A is an exemplary spectrogram indicating the presence of different gas species.

[0025] Figure 10B is related to Figure 10A a graph that depicts the variation of signal intensity over time.

[0026] Figure 11A and Figure 11B are flowcharts depicting methods for detecting limited amplitude spectral signals in a processing chamber of a semiconductor device manufacturing apparatus according to some embodiments.

[0027] Figure 12 is a flowchart showing another method for indirectly determining chamber conditions of a processing chamber of a semiconductor device manufacturing apparatus according to some embodiments.

[0028] Figure 13 shows a schematic diagram of components of a computing device implemented in a computing system according to some implementations. DETAILED DESCRIPTION

[0029] The present disclosure relates to characterizing semiconductor equipment, such as a multi-station processing chamber. Current methods for sensing and monitoring the state of semiconductor equipment typically involve using sensors for corresponding tasks or task categories. Measurement results obtained from various sensors are used individually to obtain or infer an indication of one or more characteristics related to the equipment. In some cases, expert judgment is used to determine characteristics based on experience or intuition, or based on results obtained from other sensors or techniques. That is, in some cases, sensing techniques may require a human factor to obtain meaningful data.

[0030] In some common scenarios related to processing engineering, processing performance checks are performed by, for example, visually verifying how the plasma behaves in some way based on the response of a sensor to one of hundreds of relevant channels when viewed through a viewport, or (most commonly) by the performance on a substrate (e.g., a 300 mm wafer) after processing. These methods do not provide real-time responses. Engineers do not always have time to check the plasma for every recipe or recipe step, and in a rapidly changing system, it may not be easy to understand subtle system offsets through visual inspection because the order of magnitude of plasma dynamics is from microseconds (μs) to milliseconds (ms). Without an automated system, continuous monitoring of relevant channels is not feasible, and implementations using current sensors may not capture subtle changes, or subtle changes may be regarded as noise in the system. The most reliable (and most commonly used) indication of processing variation is the impact that a particular recipe has after processing. Using the response of the characteristics on the wafer (deposited film thickness, refractive index (RI), etc.), metrology after processing indicates whether the system state has changed. However, not all wafers are measured after processing. These sensing inefficiencies can lead to yield impacts or wafer scrapping, and do not make real-time control feasible.

[0031] In some common scenarios regarding the clean endpoint (i.e., the endpoint of chamber cleaning), endpoint detection relies on timed cleaning (which does not account for system or process variability and / or the accumulation of changes based on different processes), or uses narrow-band absorption techniques such as infrared endpoint detection (IR-EPD). IR-EPD looks for specific voltages and signal slopes or other absorption indicators, and adds an over-etch step. The over-etch step may cause significant etching in some areas of the workstation, thereby shortening the service life of the susceptor, for example due to the formation of aluminum fluoride (AlF3). Another method for endpoint detection in chamber cleaning involves using visual signals, such as using a camera. However, such vision-based detection is limited to the visible area, and accurately determining which area is the slowest or the last part to clean requires extensive verification.

[0032] Therefore, a sensing method is needed that can automatically acquire and provide continuous and accurate signals to provide a better understanding of the state of the equipment system and provide opportunities for control.

[0033] The following terms are used throughout this specification:

[0034] "Manufacturing equipment" refers to the equipment in which manufacturing processes are carried out. Manufacturing equipment typically has a processing chamber, in which a workpiece is located during processing. Generally, in use, manufacturing equipment performs one or more semiconductor device manufacturing operations. Examples of manufacturing equipment for semiconductor device manufacturing include deposition reactors, such as electroplating units, physical vapor deposition reactors, chemical vapor deposition reactors, and atomic layer deposition reactors, and subtractive processing reactors, such as dry etching reactors (e.g., chemical and / or physical etching reactors), wet etching reactors, and ashers. In some embodiments, the manufacturing equipment can be a multi-station processing chamber having, for example, four stations.

[0035] As mentioned herein, manufacturing equipment is sometimes abbreviated as "processing chamber". In many embodiments, the processing chamber is typically a sealed enclosure in which a substrate is fixed during processing. The processing chamber can include components related to the delivery and removal of gases. It can also include components related to generating plasma and controlling plasma characteristics within the chamber. It can include components for controlling pressure (including evacuating the chamber). In the context of the present disclosure, the processing chamber can include a pedestal on which the substrate is located when being processed. The pedestal can be equipped with a chuck, such as an electrostatic chuck, to position the substrate during processing.

[0036] "Semiconductor device manufacturing operation" as used herein is an operation performed during the manufacture of a semiconductor device. As mentioned herein, such a manufacturing operation is sometimes abbreviated as "processing" or "treatment". Examples of processing include depositing a material on a substrate, selectively etching a material from a substrate, and ashing a photoresist on a substrate. Generally, the entire manufacturing process includes a plurality of semiconductor device manufacturing operations, each performed in its own semiconductor manufacturing tool, such as a plasma reactor, electroplating unit, chemical mechanical planarization tool, wet etching tool, etc. The categories of semiconductor device manufacturing operations include subtractive processing, such as etching processing and planarization processing, and additive processing, such as deposition processing (e.g., physical vapor deposition, chemical vapor deposition, atomic layer deposition, electrochemical deposition, electroless deposition). In the context of etching processing, substrate etching processing includes processing of an etch mask layer, or more generally, includes processing of any material layer previously deposited on and / or otherwise remaining on the substrate surface. Such etching processing can etch a stack of layers in the substrate.

[0037] The terms "semiconductor wafer", "wafer", "substrate", "wafer substrate", and "partially fabricated integrated circuit" may be used interchangeably. One of ordinary skill in the art understands that the term "partially fabricated integrated circuit" may refer to a semiconductor wafer during any of the many stages of integrated circuit fabrication thereon. Wafers or substrates used in the semiconductor device industry typically have a diameter of 200 mm, or 300 mm, or 450 mm. In addition to semiconductor wafers, other workpieces that may utilize the disclosed embodiments include a number of 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, microelectromechanical devices, etc. Workpieces may have a variety of shapes, sizes, and materials.

[0038] Figure 1A A fabrication tool is shown, which is represented as a substrate processing apparatus 100. In various embodiments, the substrate processing apparatus 100 may be configured to deposit a film on or over a semiconductor substrate 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). In various embodiments of the present disclosure, multiple sensors (e.g., 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 in an integrated sensor "package" or sensor system, which may be used for a variety of purposes and tasks in a semiconductor system, such as a fabrication tool, which may include one or more processing chambers and / or stations (e.g., Figure 1B a multi-station fabrication tool 150 of Figure 1C a fabrication system 182). Such sensors may be positioned or otherwise configured to sense plasma or monitor conditions in-situ (which means without moving the wafer from the processing chamber to a separate metrology chamber after processing to sense or monitor) and in real-time (i.e., when processing is performed on the wafer and on a time scale comparable to the time scale of events occurring in the processing chamber).

[0039] The substrate processing apparatus 100 may include one or more sensors or sensor packages 117 located on the chamber walls. A given sensor system may be implemented as a type of sensor, such as the type of spectroscopic sensor described further below. When implemented as a sensor package, the element 117 may include two or more different sensors, which in some embodiments may be different types of sensors. In some embodiments, the sensors include two or three of the following sensor types: spatial sensors, spectroscopic sensors, and temporal sensors. The sensor or sensor package 117 may be configured to capture image data from the interior of the apparatus 100. Note that while the sensor or sensor package 117 is shown as a single block, it represents embodiments where one, two, or more sensors are close to each other, optionally sharing a single viewport or other viewports. In some cases, the individual sensors within the block representing the sensor or sensor package 117 are trained on different components or fields of view within the chamber interior. In some cases, the individual sensors within the block 117 may be configured to capture different corresponding spectral ranges (far IR, near IR, visible light, UV, etc.).

[0040] Figure 1A The processing apparatus 100 may utilize a single processing station 102 of the processing chamber, which has a single substrate holder 108 (e.g., a susceptor) within its internal volume, and this internal volume may be maintained under vacuum by a vacuum pump 118. The showerhead 106 and the gas delivery system 101 (which is fluidly coupled to the processing chamber) may allow for the transfer of, for example, film precursors, as well as carrier and / or purge and / or processing gases, secondary reactants, and the like.

[0041] In Figure 1A it, the gas delivery system 101 may include a mixing vessel 104 for mixing and / or conditioning the processing gases for delivery to the showerhead 106. More than one mixing vessel inlet valve 120 may control the introduction of the processing gases into the mixing vessel 104. Specific reactants may be stored in liquid form before being vaporized and then delivered to the processing station 102 of the processing chamber. Figure 1A Embodiments of it may include a vaporization point 103 for vaporizing the liquid reactants to be supplied to the mixing vessel 104. In some implementations, the vaporization point 103 may include a heated liquid injection module. In some other implementations, the vaporization point 103 may include a heated vaporizer. In still other implementations, the vaporization point 103 may be eliminated from the processing station. In some implementations, a liquid flow controller upstream of the vaporization point 103 may provide for controlling the mass flow rate of the liquid to be vaporized and delivered to the processing station 102.

[0042] The showerhead 106 is operable to dispense a process gas and / or reactant (e.g., a film precursor) toward a substrate 112 at a processing station, the flow rate of which is controlled by more than one valve (e.g., valves 120, 120A, 105) upstream of the showerhead. In Figure 1A the illustrated embodiment, the substrate 112 is depicted as being located below the showerhead 106 and is shown as being placed on a pedestal 108. The showerhead 106 can include any suitable shape and can include any suitable number and arrangement of ports to dispense the process gas to the substrate 112. In some embodiments involving two or more stations, the gas delivery system 101 can include valves or other flow control structures upstream of the showerhead that can independently control the flow of the process gas and / or reactant to each station such that the gas flow can be switched to one station while prohibiting gas flow to a second station. Additionally, the gas delivery system 101 can be configured to independently control the process gas and / or reactant delivered to each station in a multi-station apparatus such that the gas compositions provided to different stations are different; for example, at the same time, the partial pressures of the gas components can vary between multiple stations.

[0043] In Figure 1A an implementation, the gas volume 107 is depicted as being located below the showerhead 106. In some implementations, the pedestal 108 can 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 spacing between the pedestal 108 and the showerhead 106 is sometimes referred to as the "gap". Optionally, the pedestal 108 can be lowered and / or raised during portions of the deposition process to regulate the processing pressure, reactant concentration, etc. within the gas volume 107. The showerhead 106 and the pedestal 108 are depicted as being electrically coupled to an RF signal generator 114 and a matching network 116 to couple power to a plasma generator. Thus, the showerhead 106 can serve as an electrode for coupling RF power into the processing station 102. The RF signal generator 114 and the matching network 116 can operate at any suitable RF power level, which can operate to form a plasma having a desired composition of radical species, ions, and electrons. Additionally, the RF signal generator 114 can provide RF power having more than one frequency component, such as a low frequency component (e.g., less than about 2 MHz) and a high frequency component (e.g., greater than about 2 MHz). In some implementations, appropriate hardware and / or appropriate machine-readable instructions in a system controller can be utilized to control plasma ignition and maintenance conditions, and the system controller can provide control instructions via an input / output control instruction sequence. However, in certain cases, plasma ignition in the chamber is not required. In some such cases, the species in the processing chamber can be electrically excited but not necessarily in a plasma state. For example, as will become relevant below, during chamber cleaning, light generated from radical or excited species based on a remote plasma source can be used.

[0044] Generally, the disclosed embodiments can be implemented with any plasma-assisted manufacturing tool, including with an integrated sensor (which includes, for example, sensor or sensor package 117) configured to acquire data related to the plasma and / or plasma phenomena (including, for example, images). Exemplary deposition apparatuses include, but are not limited to, apparatuses from the product family, the product family, and / or the product family, the product family, the product family, and the product family, each of which is available from Lam Research Corp. of Fremont, Calif., or any of a variety of other manufacturing tools that utilize a plasma.

[0045] Additionally, in some embodiments, the sensors or sensor packages described herein can be used for multiple purposes. As an example, one or more spectral sensors can collect spectral information that can be used for one or more of a variety of use applications depending on the context, such as those use applications described below. As an example, the sensor or sensor package can be or can include a hyperspectral sensor (configured to capture intensity values in multiple narrow wavelength intervals) or a multispectral sensor (configured to capture intensity values over a broader wavelength interval, typically fewer than the multiple narrow bands of hyperspectral sensing). The hyperspectral or multispectral sensor can thus provide spatial and spectral sensor information, where the spectral information can correspond to wavelengths within or outside the visible spectrum (e.g., at least a portion of the ultraviolet (UV) spectrum, at least a portion of the infrared (IR) spectrum) (infrared spectrum).

[0046] For simplicity, Figure 1A processing device 100 is shown as a stand-alone station 102 of a processing chamber for maintaining a low-pressure environment. However, some manufacturing tools employ multiple processing stations, such as Figure 1B the processing stations shown.

[0047] Referring Figure 1B , an implementation of a multi-station manufacturing tool 150 is depicted according to some embodiments. In some embodiments, the multi-station manufacturing tool 150 can employ a processing chamber 165 that includes a plurality of manufacturing processing stations, each of which can be used to perform processing operations on a substrate held in a wafer holder (such as Figure 1A the pedestal 108) at a particular processing station. At Figure 1BIn the implementation scheme, the processing chamber 165 is shown as having four processing stations 151, 152, 153, and 154. However, in some other implementation schemes, the multi-station processing device may have more or fewer processing stations, depending on the implementation, for example, the desired level of parallel wafer processing, size or space limitations, or cost limitations. Figure 1B The substrate handling robot 175 is also shown, which can operate under the control of the system controller 190. The substrate handling robot 175 can be configured to move wafers from the wafer cassette at the load port 180 ( Figure 1B (not shown in the figure), enter the multi-station processing chamber 165, and place them on one of the processing stations 151, 152, 153, or 154.

[0048] As Figure 1B shown, the processing station 153 has an associated sensor or sensor package 121, which is located within the processing station 153 and is configured to obtain in-situ information (e.g., images, spectra, and / or time data) from within the processing station 153, and in some embodiments, obtain the above information from within the processing chamber 154. The processing station 151 may have two associated sensors or sensor packages 123 and 125. The sensor or sensor package 123 is positioned and configured to obtain in-situ information from within the processing station 151 and, in some embodiments, from within the processing chamber 152. The sensor or sensor package 125 is positioned and configured to obtain in-situ information from within the processing station 151 and, in some embodiments, from within the processing chamber 153. The processing station 152 may have an associated sensor or sensor package 127, which is positioned and configured to obtain in-situ information from within the processing station 152 and, in some embodiments, from within the processing chamber 154. When implemented as a sensor package, the elements 121, 123, 125, 127 may include two or more different sensors, which may be different types of sensors. Any one or more of the sensors or sensor packages 121, 123, 125, or 127 may be coupled to the interior of the processing chamber 165 via a viewport or other window provided in the chamber wall. Additionally, although not shown in Figure 1B the figure, some embodiments may include one or more sensors or sensor packages adjacent to the processing station 154. The exemplary positioning of the sensors or sensor packages will be further described below with reference to FIG. 2 and Figure 3 FIG. 3.

[0049] In the context of the present disclosure, the terms precision, stability, and matching may refer to the sigma (σ, standard deviation) of process metrics (measurements) reported by a sensor or sensor package in high volume manufacturing (HVM). On the surface, multiple replicas of a sensor or sensor package (measurement device) are used to repeatedly measure the same process over an extended period of time. The same sensor package may be installed at multiple stations within a process module and at multiple process modules, where the process modules are installed on multiple tools of multiple HVM production lines that may be located at 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 precision off-line metrology techniques. Such correlations can then enable near real-time process optimization and root cause analysis of process variations.

[0050] The measurement results or metrics can be recorded by a host device (e.g., a computer device) that receives the data. Precision can be defined as the sigma of a metric that is repeated under nearly identical conditions over the shortest possible time. Precision can quantify or represent the fundamental limit of a metric, and typically requires more than 10 input values of the metric to calculate the sigma, where the values are acquired in a rapid sequence without unnecessary interruptions. Stability can be defined as the sigma of a metric reported over a defined period of time. Stability can quantify or represent a process study or gage study (which indicates the repeatability and reproducibility of measurements), which typically occurs over multiple days, e.g., over the duration of a process suite. For many metrics, process stability and gage stability may not be easily separable in the data. Careful use of the stability results of multiple metrics can help identify gage offsets from process offsets. High stability is beneficial because predictive maintenance and process yield improvement must be based on metrics with known stability. Matching can be defined as the sigma of metrics from multiple sensors or sensor packages acquired over multiple days (a stability data set of a sensor subsystem). One example of matching is within a single process module (PM) that contains multiple sensor subsystems. In particular, in PM internal matching, multiple subsystems on a single PM can be used to quantify the PM matching sigma of a metric. Through inter-PM matching, matching can be measured, for example, within a manufacturing tool, across manufacturing tools, or across installation bases at multiple location points. Subsystem (e.g., a subsystem of a sensor or sensor package) specific control limits can be used to manage the non-ideality of matching, but this approach is cumbersome. Metrics tend to match well with subsystems that are functioning well. Generally, process precision is the easiest (single chamber, short time), process stability is more difficult to achieve (because it drifts over time, for example), and process variations between chambers or between tools are the most difficult.

[0051] In some implementations, a sensor or sensor package may report metrics as a function of time to a host device. This data can be further refined to define customized metrics defined by the end user of the sensor or sensor package. The accuracy, stability, and matching of the metrics help define the limits of the sensor package to manage the underlying processing. Finally, the sensor or sensor package needs to handle accuracy, processing stability, and processing matching, e.g., for developing processing metrology techniques, performing processing quality control (e.g., determining if behavior is normal, if abnormal behavior is an impulse or offset issue, if behavior requires corrective action), and establishing processing tolerance limits for metrics deemed important by the end user.

[0052] In some embodiments, a manufacturing tool 150 may include a system controller 190 configured to control the processing conditions and hardware state of the manufacturing tool 150. In some embodiments, the system controller 190 may interact with one or more sensors, a gas flow subsystem, a temperature subsystem, and / or a plasma subsystem (collectively referred to as the block representative subsystems 191) to control the processing gas flow, thermal conditions, and / or plasma conditions to be suitable for controlling the manufacturing process. In various implementations, the system controller 190 and the subsystems 191 may be used to implement recipes or other processing conditions in one or more processing stations (e.g., 151-154) of the processing chamber 165. The system controller may be located entirely on the manufacturing tool or adjacent to the manufacturing tool (e.g., as an edge computer in a manufacturing device), or the system controller may be located entirely remotely from the manufacturing tool (e.g., on a host cloud computer resource), or the system controller may be partially located on the manufacturing device and partially located remotely.

[0053] In a multi-station manufacturing tool, an RF signal generator may be coupled to an RF signal distribution unit configured to divide the power of an input signal into, for example, four output signals. The output signals from the RF signal distribution unit may have RF voltages and RF currents of similar levels, which may be delivered to the respective processing stations (e.g., 151-154) of the multi-station manufacturing tool.

[0054] Figure 1C A top view of an electronic device manufacturing system 182 having four four-station manufacturing tools 188, 189, 193, and 195 is provided. The four-station manufacturing tools 188, 189, 193, and 195 may be Figure 1BExamples of stations 151 - 154. Each four - station tool includes four processing stations, each processing station being configured to hold and process a substrate. At the front end of the electronic device manufacturing system 182 are three front - opening unified pod (FOUP) 183a, 183b, and 183c accessible by the front - end wafer handler robot 185, which is configured to transfer wafers between the FOUP and the first load lock 187. The first wafer handler 170 can be positioned and configured to transfer wafers between the first load lock 187 and the four - station manufacturing tools 188 and 189. The first wafer handler 170 can also be configured to transfer wafers to the second load lock 171, which enables the wafers to be available for the four - station manufacturing tools 193 and 195 via the second wafer handler 172.

[0055] In some embodiments, the four - station tool 195 (by way of example) can include three sensors or sensor packages 196, 197, and 198, which are disposed around its outer wall. In Figure 1C s, the sensors or sensor packages 196 - 198 are shown as being vertically fixed to three sides of the four - side chamber of the tool 195. In the illustrated embodiment, the only side without a sensor or sensor package is the side adjacent to the wafer handler 172. Although Figure 1C not shown in, a similar sensor or sensor package configuration can be provided on any one or more of each of the three other four - station chambers 188, 189, or 193 in the system. Additionally, in some implementations, sensors or sensor packages can still be provided on the side adjacent to the wafer handler 172. A variety of other positions for one or more sensors (including sensors grouped together or separately in a sensor package) are possible, depending on, for example, the measurement, angle, desired measurement location. It should be understood that in some cases, the system controller (e.g., Figure 1B of 190) can be configured to adjust the position or orientation (e.g., up, down, left, right, diagonal, azimuth, elevation) of a given sensor or sensor package. Spectral sensor configuration

[0056] Spectral sensors provide wavelength - specific radiation intensity detection. They output a value of the radiation intensity, which is a function of the wavelength or spectral region. Spectral sensors have multiple functions. The information they provide can distinguish or identify specific chemical substances, each chemical substance having its own specific spectrum. In some embodiments, spectral sensors can distinguish signals at wavelengths in one or more regions of the electromagnetic spectrum, which includes UV, visible light, and IR.

[0057] Spectral sensors can be implemented in a variety of ways. Examples of wavelength separation components include (a) a dispersive device (which separates wavelengths in a dispersive medium such as a prism), (b) a diffractive device (which uses, for example, a diffraction grating to separate wavelengths by diffraction), and (c) a filter that is placed in front of an intensity detector such that the detector only receives wavelengths of interest.

[0058] An example of a spectral sensor detector is a linear array of optical detectors that is configured to detect wavelength-specific intensities at different detectors in the linear array. The optical detectors can be configured to provide an output of intensity as a function of wavelength. A wavelength separation component placed in front of the linear array provides one-dimensional (along the linear array) wavelength separation such that each component of the linear detector is associated with a specific wavelength or wavelength range.

[0059] Unlike spatial sensors, spectral sensors do not need to provide a multi-dimensional image. Additionally, for some applications, the spectral sensor does not need to acquire data quickly (e.g., a capture rate of ≥1 ms or approximately 1 ms to 1 s).

[0060] Figure 2ADepict an exemplary implementation of a spectral sensor 210 having a processing chamber (e.g., 165, 182) or station (e.g., 102, 151 - 154, 188, 189, 193, 195). Such a spectral sensor may also be referred to as a spectrometer and is configured to separate and measure spectral components (e.g., wavelengths). The spectral sensor 210 may be coupled to a wall 212 of the chamber via an optical fiber 214, and the optical fiber 214 may be coupled to an optical system 216. In some embodiments, the optical system 216 may include light collection optics (e.g., lenses or other devices for focusing, dispersing, or transmitting light), which receive light and / or spectral information via a viewport or other window 218. The light and / or spectral information may be generated by the presence of processing, gases, and other materials in the chamber (e.g., on the inner side of the wall), reactions with the plasma 207 or other materials present in the chamber, etc. The collected light and / or spectral information may be transmitted via the optical fiber 214 and received by the spectral sensor 210. Then, the spectral sensor 210 may be implemented as described above to separate, for example, the components of the received light and / or spectral information. Spectrum 220 is an example of a spectrum that may be generated based on the detected components of the light and / or spectral information. As shown, the spectrum 220 may represent the intensity of wavelength components. Generally speaking, the foregoing may be referred to herein as spectral information or spectral characteristics and includes the position of wavelengths, spectral lines, or bands (e.g., relative to the wavelength along a spectrum or spectrogram such as 220), the intensity or amplitude of spectral lines or bands, and / or the width of spectral lines or bands.

[0061] Figure 2B is a diagram of an exemplary spectral sensor 210 according to some embodiments. In some embodiments, the spectral sensor 210 may be at least partially implemented as, for example, an OES sensor or a spectral reflectometer, or at least implemented as part of, for example, an OES sensor or a spectral reflectometer, and in some cases may be referred to as a spectrometer. The spectral sensor 210 may be coupled to one or more transmission optical fibers 232. The transmission optical fibers 232 may be Figure 2A an example of the optical fiber 214 and may receive spectral information from corresponding processes, for example, at a processing chamber or at a station within the processing chamber. For example, the transmission optical fibers 232 may receive spectral information from a multi - station processing chamber (e.g., of the type described with respect to Figure 1B and 1C during processing via light collection optics 233. The light collection optics 233 may be Figure 2A an example of the optical system 216. In some embodiments, the light collection optics 233 may include lenses or similar devices and / or the terminal portions of the optical fibers 232. In some embodiments, each of the four optical fibers 232 is associated with its respective processing station and collects the optical signals emitted from its processing station. AsFigure 2B As shown, as an implementation, the spectral sensor 210 can receive spatial information from four stations of a four-station processing chamber via four transmission optical fibers 232. Figure 2B Four processing stations and four optical fibers 232 are depicted, but any number of processing stations and optical fibers can be implemented, and more than one optical fiber can be implemented for a given processing station (e.g., two or more optical fibers coupled to different surfaces or walls of the processing station). At least some of the transmission optical fibers 232 can be grouped and received by the spectral sensor 210 via at least one inlet 234 associated with the spectral sensor 210.

[0062] In some embodiments, the spectral sensor 210 can also include dispersive optics, such as one or more mirrors 235, some or all of which can have optical dispersion characteristics by being coated to different depths, such that different wavelengths have different penetration lengths, thereby allowing the mirrors 235 to reflect light of different wavelengths. In some embodiments, as described above, the dispersive optics can include diffraction devices and / or filters, as well as other dispersive devices (e.g., prisms). The spectral sensor 210 can include an image sensor 236 for detecting spatial information, which can include spectrally separated light components. For example, the spatial information can indicate that wavelength A has an amplitude of X and wavelength B has an amplitude of Y, and so on for other separated wavelengths and their respective amplitudes.

[0063] In some embodiments, the optical detector for the spectral sensor (e.g., the image sensor 236) is a linear, one-dimensional array or a two-dimensional array of detector devices. For example, an OES sensor is a two-dimensional charge-coupled device (2D CCD) array, where spectrally separated light components are detected by different regions of the 2D CCD. In some implementations, a linear array of detectors can be used, which individually detects wavelength-specific intensities at different detectors in the linear array. Refraction (e.g., a prism) or diffraction (e.g., a grating) can be employed to perform wavelength separation in one dimension along the linear array. Each element of the linear detector can be associated with a specific wavelength or wavelength range. Multiple implementations can be adopted for different use cases. In some embodiments, the spectral sensor for capturing spectral information about a plasma is capable of sensing the intensity values of electromagnetic radiation of 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, and / or distinguishing signals from wavelengths including the same. In some cases, the optical detector does not need to provide a fast capture rate (e.g., more frequently than about every 1 ms or longer). The optical detector can operate at a frame rate of, for example, about every 1 ms to 1 second.

[0064] A variety of other implementations of the spectral sensor 210 are possible, including, for example, mounting the spectral sensor directly at the viewport, removing at least some of the transmission optical fibers 232, removing some or all of the light collection optics 233, switching between different optical inputs (e.g., fiber optic switches, microelectromechanical systems (MEMS) mirrors), using different types of dispersive optical device configurations (e.g., gratings, prisms, computed tomography (CT), echelle gratings), using spectral filters (e.g., filter wheels, or in some cases etalons or tunable liquid crystals), using time-domain spectroscopy (e.g., in some cases, using a spectral analyzer).

[0065] In some embodiments, a spectroscopic reflectometer device can be used as a spectral sensor (as will be further described below). The reflectometer device can include a light source for irradiating a surface to be processed and an optical detector. The optical detector can include one or more photodetectors. An optical fiber cable can be connected to the spectroscopic reflectometer device. The optical fiber cable can include transmission optical fibers and receiving optical fibers, where each receiving optical fiber can be connected to a respective individual photodetector. In some cases, multiple receiving optical fibers can be connected to the same photodetector.

[0066] In some embodiments, the spectral sensor 210 can be a spectroscopic reflectometer device having its own light source associated therewith.

[0067] In some embodiments, spectroscopic ellipsometry can be used. In some embodiments, reflectometers with polarization control can be used particularly with highly polarized structures.

[0068] Advantageously, as opposed to certain other types of sensors, spectral sensor intensity counts can be collected over a larger area. For example, spatial information can be based on the intensities collected through the individual pixels of the visible portion (e.g., the interior of the processing chamber) acquired by a spatial sensor (e.g., a camera).

[0069] In some embodiments, a single spectral sensor (e.g., an OES sensor) can be deployed on a single processing chamber, but it can be configured for multiple applications. Examples of such applications include monitoring a manufacturing process (for which the chamber is designed), determining whether an HF purge is complete, detecting leaks, detecting a chamber clean endpoint, and any combination thereof. The spectral information obtained using a single spectral sensor can be used in different ways depending on the context, as will be described in detail below. Lighting source

[0070] In some cases, a manufacturing tool can include a lighting system or one or more lighting sources that are configured to illuminate all or one or more parts inside the manufacturing tool. It should be noted that in some cases, instead of using a lighting system, the plasma itself is used for illumination. In some embodiments, the lighting system employs one or more light-emitting diodes (LEDs) or other light sources. The light source can be monochromatic, multi-color with discrete emission wavelengths, or broad-spectrum. The light source can operate continuously, pulse synchronously with one or more camera shutters, pulse asynchronously with one or more camera shutters, or pulse synchronously with other processing parameters (e.g., RF generator or gas delivery valve). In other implementations, multiple light sources are used at different positions on the indoor or outdoor side. The multiple light sources can be powered on continuously or sequentially, along with timing management, so as to use structured illumination to establish a super-resolution image of the features in the room. In some embodiments, one or more notch filters or bandpass filters are provided in front of the light source, so as to produce an effect that can support analysis (e.g., identifying specific chemicals by their emission spectra). In some embodiments, stroboscopic lighting and other structured lighting (e.g., using periodic lighting bursts such as 10, 30, 60 Hz) can be employed to provide additional discrete visual information at a consistent frequency. Stroboscopic lighting of a specific wavelength can also support the calibration of spectral sensors.

[0071] In some implementations, the lighting source is used in combination with the various embodiments disclosed herein and the applications described below. Some examples include, but are not limited to, (i) measuring the reflectivity of an LED on the inner surface to identify surface conditions (e.g., the presence and quality of a coating), (ii) calibrating a spectral sensor (as described above with respect to stroboscopic lighting), and (iii) in-situ absorption measurement of substances. The lighting source can also be used to confirm whether a spectral sensor is operating properly, track the drift of the system over time, and determine whether the sensor or the lighting source is the source of system attenuation. Applications and Methods of Spectral Sensors

[0072] The characteristics of spectral sensors in typical applications lie in conservative or narrow processing applications. That is, the processing applications can be carried out for a preset duration without feedback. A spectral sensor can be used to collect spectral information to obtain information about a manufacturing system or tool. For example, a spectral sensor with a line of sight to an accessible processing chamber can be used to monitor spectral information. Post-processing metrology can be performed to determine or obtain basic insights, such as any changes in the system state.

[0073] In addition, existing technologies for monitoring conditions in a processing chamber are typically applicable to only one application or are only used to detect one substance in the processing chamber. For example, mass spectrometry is commonly used to detect trace gases. Mass spectrometers are sometimes limited to specific substances, expensive because they need to be integrated into a vacuum system and are invasive, and are vulnerable to contamination in chemically harsh environments. As another example, infrared endpoint detection (IREPD) can be used to detect a single chemical substance related to a processing endpoint. Specifically, IREPD employs a filter designed to detect the light absorption of a specific substance; it is not practical for detecting substances other than a selected substance. IREPD also requires IR source light.

[0074] Some existing technologies miss local events in the processing chamber. For example, IREPD and some other known technologies sample the gas in the exhaust line, where it is a mixture of gases from the entire chamber and that have resided in the chamber for multiple durations.

[0075] On the other hand, spectral sensing can passively sense information to determine chamber conditions, plasma conditions, the presence of gases, etc. Spectral sensing can also simultaneously sense many substances at low cost and without invading the processing volume (e.g., the sensor can be disposed outside the processing chamber, e.g., through a window). In addition, spectral sensing has high sensitivity, which is practical because certain deposition processes are very sensitive to trace contaminants.

[0076] For example, optical emission spectroscopy (OES) measures the spectral content of light or other emissions, e.g., from a processing chamber. The spectrum of the plasma generated within a deposition tool (obtained using a spectral sensor) can be used to evaluate the plasma state and / or the chamber state (as will be described below). Process Diagnostics

[0077] In one exemplary aspect and application of spectral sensing according to the embodiments described herein, spectral sensing (e.g., OES) can be used to monitor processing conditions. Historically, such processing conditions have not been measured or have been measured through cumbersome manual or ad-hoc procedures. Some existing technologies may involve using limited measurement methods such as mass spectrometry or IRPED. However, spectral sensing can be used passively for diagnostic and / or troubleshooting purposes or be used as part of an active control loop.

[0078] Process diagnostics refers to the ability to understand the state of processing conditions within a manufacturing tool and to evaluate the tool configuration. Examples of processing conditions include the gas currently flowing into the processing volume. Other examples of processing conditions include the presence of gases, the times for introducing or removing gases from the chamber volume, the plasma activation (on) or non-activation (off) state, the gas temperature, and plasma parameters. Examples of tool configuration include which gas line is connected to which valve.

[0079] The processing conditions can be evaluated by existing sensors (e.g., current and voltage probes, thermocouples, and manometers). In some cases, understanding of the processing conditions can rely on expertise, such as differentiating processing gases based on appearance color. However, for many cases, existing methods are insufficient. For example, evaluating processing gases by observable color is essentially a subjective technique and generally cannot distinguish gas mixtures. Tool configuration is also difficult to evaluate. For example, the pipeline conveyance of processing gases is manually handled and highly dependent on administrative control. The means for confirming the connection of the correct gas pipeline to the correct valve are limited.

[0080] For this reason, spectral sensors can be used to measure many parameters governing tool configuration and processing conditions. Referring back to the example of tool configuration, spectral sensing (e.g., using OES) can be used to identify which gas pipelines are connected to which valves. In some methods, each valve can be independently opened, and RF power can be applied to the chamber at a known gas pressure. The resulting spectral characteristics can be compared with a reference library of spectral lines, patterns, and / or full spectra of the processing gases of interest (either manually or by computer calculation, e.g., matching wavelengths and intensities). In some cases, the spectral response of the system due to accumulation or etching of the window can be corrected by using a reference light source or plasma.

[0081] In some cases, process optimization may require in-situ measurement of the time required to introduce a new processing gas or the time required to evacuate the processing gas from the chamber volume. This can be accomplished by methods similar to those described above. In some methods, a plasma can be ignited in a known gas under known conditions, and then the valve for the processing gas in question can be opened or closed. Then, spectral sensing (e.g., using an OES sensor) can be used to track the characteristics (spectral lines, spectral bands, etc.) of the gas in question to determine how long it takes for the gas to reach the processing volume or to evacuate it from the processing volume. In some implementations, the characteristics can include dominant features that have a significant presence in the spectrum.

[0082] Figure 3 An exemplary spectrum 300 obtained using spectral sensing is depicted, which depicts the spectral signal obtained over time with respect to wavelength. It can be seen that after a certain amount of time 302, the plasma RF power is turned on for a period of time 304, during which the spectral sensor can detect emissions from plasma activation. The spectral sensor can use the methods described above Figure 2A - 2BThe described implementation separates components. Taking RF activation as an example, it can be seen that the corresponding spectral pattern 310 includes a band 312 of higher intensity within a specific wavelength range, along with some dominant features of higher irradiation intensity. In some cases, the obtained spectral pattern 310 can be compared or mapped to a reference library of spectral lines, patterns, and / or full spectra. In other words, if the spectral sensor obtains the spectral pattern 310, it can be determined with a certain degree of certainty or probability that the plasma RF power is on. Therefore, chamber conditions, such as the plasma on state, can be determined based on this type of spectral sensing.

[0083] Figure 3 Additional spectral patterns 320, 330 corresponding to chemical changes (e.g., gas flow) and power changes are depicted. By comparing the spectral patterns to, for example, this reference library, these chamber conditions can be similarly mapped and determined using spectral sensing. There are no spectral characteristics when the plasma RF is off because this chamber condition does not cause emission. Based on the presence or absence of spectral lines and intensities, the presence or absence of certain chamber conditions can be determined. As an example of spectral intensity, spectral lines 352 and 354 at similar wavelengths have different thicknesses and brightnesses. In some implementations, this difference can be determined through visual analysis (e.g., comparing pixel intensities and counts) to distinguish different chamber conditions; in this case, these chamber conditions are chemical changes (where line 352 is darker than line 354) and power changes (where line 354 is more prominent than line 352).

[0084] Furthermore, the obtained spectrum (e.g., via OES) is a sensitive and complex metrology for process conditions. Specific process quantities, such as gas temperature, plasma temperature, and discharge mode, can be directly obtained from spectral information. Alternatively, process health can be evaluated by comparing to a database of known good spectra or by machine learning algorithms that identify correlations between spectral characteristics and on-wafer performance. Examples of machine learning models that can achieve such identification include models trained for logical classification using labeled spectral patterns and loss minimization. Process Control

[0085] Process control refers to the active control of the conditions of a deposition tool to obtain the desired wafer results. Typically, process control is achieved through open-loop tool operation and off-tool measurements. For example, film properties (stress, thickness, uniformity, etc.) are measured on a metrology tool, and this information is used to iterate the process conditions. However, this process control method is fundamentally slow (e.g., taking several days) and difficult to adapt to slow drifts in tool performance.

[0086] In the process of processing diagnosis (as described above), the use of spectral sensing (e.g., OES) is considered to play a purely passive or diagnostic role to evaluate tool or plasma conditions. For process control, spectral sensing can be used as part of an active control scheme. Since the spectrum (e.g., 300, 310) strongly depends on the conditions within the deposition system, spectral sensing can be used to relatively quickly (and directly) determine whether the chamber is operating under the desired conditions (with the desired temperature, density, etc.). If the plasma deviates from the optimal processing conditions, the processing parameters can be iteratively adjusted by comparing with known good spectra, thus bringing the processing back to the desired state. In some implementations, this may involve repeated comparisons, which can be added to the ontology of the labeled data for further training of the aforementioned machine learning classification model. In certain cases, the selection of how to adjust the processing parameters can be determined by an empirical model of how each parameter affects spectral characteristics, a theoretical model of plasma performance, or a combination of both.

[0087] Figure 4A and Figure 4B are flowcharts that depict methods 400 and 450 for monitoring and controlling semiconductor device manufacturing equipment according to some embodiments. One or more of the functions of methods 400 and 450 can be performed or initiated by a computerized device or system. The structure for performing the functions depicted in one or more of the steps shown includes the hardware and / or software components of such a computerized device or system, e.g., a controller device, a computerized system, or a computer-readable device, which includes a storage medium storing computer-readable and / or computer-executable instructions that are configured to cause at least one processor device or computerized device to perform those operations when executed by a processor device. A controller can be an example of a computerized device or system. The controller can be connected to a tool computer, or an independent edge node having components similar to those of the tool computer, but the logic and storage can be independent of the tool controller. A subsystem (e.g., 191) can be another example of a computerized device or system. A processing chamber can be another example of a computerized device or system. Exemplary components of the processing chamber and the controller are depicted in Figure 4A and Figure 4B and are described in more detail elsewhere herein. Figure 1A and Figure 1B and Figure 13 respectively, and are described in more detail elsewhere herein.

[0088] It should also be noted that the operations of these methods 400 and 450 can be performed in any suitable order, not necessarily the Figure 4A and 4B depicted order. In addition, methods 400 and 450 can include more steps than Figure 4A and 4BMore or fewer operations as depicted therein, thereby monitoring and controlling semiconductor device manufacturing equipment.

[0089] In some embodiments, the computerized device or system may include at least one spectral sensor of a semiconductor device manufacturing apparatus.

[0090] Referring Figure 4A , at block 402, method 400 may include starting a process. In some cases, the process may include a deposition process, such as introducing a gas into a processing chamber and / or depositing a material (e.g., a film) on a substrate.

[0091] At block 404, method 400 may include collecting spectral data. In some embodiments, at least one spectral sensor (e.g., an OES sensor) may be used to collect spectral data. The spectral sensor may be Figure 2A and Figure 2B an example of the spectral sensor 210, or a spectral reflectometer device. Such a spectral sensor may be disposed outside a processing chamber of a semiconductor device manufacturing apparatus and have access to the interior of the apparatus via, for example, a viewport or other window.

[0092] At block 406, method 400 may include determining whether desired process conditions have been achieved. Examples of process conditions include the presence of a gas, the current gas flowing into the processing volume, the time for introducing a gas into or purging it from the chamber volume, a plasma activation (on) or non-activation (off) state, gas temperature, and plasma parameters.

[0093] In some embodiments, determining whether desired process conditions have been achieved may include obtaining one or more spectra based on light or electromagnetic radiation emission from a semiconductor device manufacturing apparatus and evaluating the spectra. In some implementations, such an evaluation may include comparing a spectral pattern (e.g., spectral lines and intensities of spectral lines) with known spectral information (e.g., a reference library of spectral lines, patterns, and / or full spectra), as discussed with respect to Figure 3 .

[0094] If the desired process conditions have not been reached, then at block 408, method 400 may include adjusting the process. In some cases, the process may be adjusted by, for example, increasing or decreasing the gas flowing into the processing chamber, increasing or decreasing the gas flow rate, activating or deactivating the plasma (e.g., by increasing, decreasing, or stopping the plasma RF power), changing plasma parameters, increasing or decreasing the gas temperature and / or pressure within the processing chamber, or changing the configuration of the manufacturing tool (including, for example, repairing components such as leaking gas valves or gas lines). Those of ordinary skill in the art to which the present invention pertains may envision other adjustments to achieve the desired process conditions.

[0095] If the desired processing conditions have been reached, at block 410, method 400 may include determining whether to end the processing. If so, the processing ends at block 412. If not, method 400 may return to block 404 to continue collecting additional spectral data.

[0096] At block 452, method 450 may include using at least one spectral sensor to detect spectral characteristics of emissions within an interior portion of a semiconductor device manufacturing apparatus. In some embodiments, the spectral characteristics may include patterns of spectral lines and intensities, as discussed with respect to Figure 3 discussed.

[0097] At block 454, method 450 may include adjusting a process associated with the semiconductor device manufacturing apparatus toward the desired processing conditions in response to determining, based on the detected spectral characteristics, that the desired processing conditions within the interior portion have not been met.

[0098] In some embodiments, method 450 may include determining whether to end a process associated with the semiconductor manufacturing apparatus in response to determining, based on the detected spectral characteristics, that the desired processing conditions within the interior portion have been met.

[0099] Method 450 may further include, in response to determining not to end the process, using at least one spectral sensor to detect additional spectral characteristics of emissions within the interior portion, or ending the process in response to determining to end the process. Chamber Clean Endpoint Detection

[0100] In another exemplary aspect and application of spectral sensing in accordance with the embodiments described herein, spectral sensing (e.g., OES) may be used to determine a chamber clean endpoint. Accurate detection of a chamber clean endpoint is needed when operating a processing chamber. Processes performed within the processing chamber (e.g., depositing a conformal material film onto a substrate using a chemical vapor deposition (CVD) process) may not only result in film deposition on the substrate, but also in film deposition on various chamber surfaces as by-products of such processes. Over time, the buildup of undesired deposits on the chamber surfaces can lead to particles and potential contamination, which can have a negative impact on wafer yield. Thus, chamber cleaning is important for the repeatable operation of deposition tools.

[0101] The processing chamber is cleaned periodically to remove the buildup of such byproduct particles. The removal of chamber deposits can be accomplished, for example, by reacting a film of trace byproducts with a reactive gas (e.g., radical fluorine, oxygen) to produce silicon tetrafluoride (SiF), which can subsequently be removed from the chamber. The optimal cleaning time for a given chamber depends on many factors, including the type of deposited material, temperature, pressure, reactive gas delivery, processing gap spacing (e.g., between the substrate and the showerhead), etc. It would be advantageous to determine the endpoint of chamber cleaning to prevent overcleaning due to, for example, reactions with these surfaces themselves. Chamber cleaning also ensures that the chamber is in a known state before the deposition process begins and that the system returns to a known state after the deposition process.

[0102] Chamber cleaning can be performed (e.g., periodically after precursor chemicals introduced into the processing chamber are deposited on the substrate and / or inner surfaces) to maintain the life of the susceptor and / or improve the performance of deposition or other processes. However, as described above, trace chemical byproducts always gradually and incrementally accumulate on the components (e.g., walls, susceptor, or showerhead) of the processing chamber.

[0103] The duration and type of cleaning required may vary depending on the operating history. However, current chamber cleaning methods involve timed cleaning (which does not account for system or process variability and / or changes in accumulation based on different processes) or the use of an infrared endpoint detector (IR-EPD). In cases where the chamber cleaning runs for a fixed time, they must always run for too long to ensure consistency, at the cost of, for example, accelerated tool degradation and downtime due to overcleaning or overetching. A cleaning endpoint needs to be detected to stop the etching process so that the buildup is completely removed but not beyond its scope (without etching the walls or the susceptor itself due to overetching). In addition to timed cleaning, single-use sensors (e.g., IR-EPD) can be used to measure the concentration of cleaning byproducts to detect the cleaning endpoint. The IR-EPD looks for specific voltages and signal slopes and incorporates an overetch step. The IR-EPD can cause significant etching in certain areas of the chamber, thereby shortening the life of the susceptor, for example, due to the formation of AlF3. The IR-EPD also significantly increases the tool cost per application and can only monitor the condition of the "entire chamber" rather than individual parts of the chamber (e.g., the walls). This is because the IR-EPD measures the effluent of the entire reactor and thus can hardly correlate the measurement results with specific regions. However, in some cases, OES can be used to obtain more local information by using, for example, a limited field of view.

[0104] In some embodiments of the present disclosure, a spectroscopic sensor (e.g., an OES sensor) can serve as the primary sensor for indicating the cleaning status. Spectroscopic sensing can infer the status of the chamber, e.g., at the walls. Generally, spectroscopic sensing can provide endpoints for a variety of cleaning methods, including remote, direct, and hybrid cleaning. Direct cleaning refers to a remote plasma source being directly attached to the chamber rather than having its outlet arranged to pass through a gas distribution system. Even when using a remote plasma source, the transport distance from the source to the processing volume is short enough such that the plasma does not cool or recombine before being delivered to the chamber. Thus, there is sufficient light available for observation. In remote cleaning, the plasma is generated by a remote source, and the resulting reactive species are transported to the volume of the processing chamber, where the reactive species diffuse to the walls and react with the coating. In hybrid cleaning, a remote plasma source is used in combination with the generation of plasma in the processing chamber volume. Although less light is generated by remote cleaning than by direct cleaning, the endpoint can be determined by measuring the low light source or by using a reference plasma. More specifically, spectroscopic sensing that provides an endpoint using any of these cleaning methods can be achieved by one of two methods.

[0105] The first method is to directly observe the recombination glow that occurs on the surface during the cleaning process. When molecules are formed in an excited state and then decay spontaneously, the surface recombination process produces a faint but observable glow near the relevant surface. The rate and spectral properties of this recombination glow depend largely on the surface state and thus change as the surface in the deposition tool is cleaned. As the cleaning progresses, the spectral signal in the light collection area may weaken, indicating the cleaning status. Thus, spectroscopic sensing (which, in some cases, is used in combination with a spatial sensor (e.g., a camera)) can provide an indication that the cleaning is complete and there has been no significant over-etching.

[0106] Certain spectral bands are related to the cleaning condition. Thus, in some implementations, the spectral information can be compared, for example, with a known reference library using the method described. Figure 3

[0107] The second method is to directly observe the cleaning by-products in the chamber volume after they are excited by a reference plasma. In certain contexts of the present disclosure, a reference plasma can refer to a plasma that is not related to the manufacturing process. During the cleaning process or at a set interval between cleaning steps, the reference plasma is ignited to excite the gas in the volume. The degree of cleaning can be directly evaluated by measuring the cleaning by-products (e.g., SiF, AlF) or indirectly evaluated by changes in spectral features (e.g., nitrogen, argon) that do not participate in the cleaning process. The change in the indirect spectral feature occurs because of changes in the wall conditions (e.g., conductivity, secondary electron emission), where the change in the wall condition affects the properties of the reference plasma.​

[0108] Advantageously, spectral sensing does not need to rely on specifically measuring cleaning by-products, although in some embodiments this can be done according to the second method described above. Additionally, spectral sensing can provide more details than IR-EPD, and spectral sensing can detect end points on various surfaces of the chamber (e.g., walls, top of the pedestal, bottom of the pedestal, showerhead). Spectral sensing can also allow for optimization of cleaning for different process histories. Different process histories may be associated with different amounts of film accumulation on the chamber walls. If the accumulation levels are different, the cleaning time will also change accordingly. However, the length of cleaning may not be strictly linearly related to the accumulation thickness. Therefore, instead of cleaning time, the "cleaning rate" can be determined and set through optimization (e.g., based on a machine learning model). For example, in some scenarios, a spectral sensor can determine which parameters (pressure, gap, flow rate, temperature, etc.) to increase or decrease, e.g., based on different machine learning models, to achieve this cleaning rate. Neural networks such as recurrent neural networks (RNNs) can also be used to vary the cleaning rate based on control constraints.

[0109] Referring Figure 5 to, which shows a graph 500 comparing signals obtained via spectral sensing (e.g., using an OES sensor) and infrared-based signals (e.g., obtained via IR-EPD). Line 502 represents the infrared-based signal, while line 504 represents the signal obtained via spectral sensing. It should be noted that the line 504 representing the spectral signal is marked by several "punch-through events" 506a - 506c, which are sudden changes in the measured light or spectral signal (e.g., in terms of intensity measurement). These punch-through events 506a - 506c can indicate that the cleaning has been completed or has breached a threshold (e.g., the composite glow is below the threshold, or the cleaning by-products exceed the threshold).

[0110] Consider a situation where etching is performed as part of chamber cleaning. Different parts of the chamber can be cleaned independently and separately during the cleaning process, rather than the entire chamber. For example, etching can be done first at the pedestal, then at the showerhead, and then at the walls (or in numerous other sequences). Breakthroughs can occur for each step, where sudden changes in chamber conditions can lead to significant changes in the spectral signal. For example, after cleaning the mandrel, the spectral signal may decrease, as indicated by the punch-through event 506a. Thereafter, after cleaning the showerhead, at another punch-through event 506b, the spectral signal can decrease again. Thereafter, after cleaning the top surface or the bottom surface of the pedestal, the spectral signal can decrease again at another punch-through event 506c. Breakthroughs may even occur after other events, such as cleaning the chamber walls. Thus, discrete cleaning events can be measured. Therefore, spectral sensing can detect one or more end points on various surfaces of the chamber.

[0111] In contrast, the IR-EPD signal (line 502) may be able to indicate an overall clean endpoint (e.g., at time 508) and an over-etch step added (e.g., at time 510), which brings a higher risk of over-cleaning compared to detecting discrete endpoints using spectral sensing (e.g., punch-through events 506a - 506c).

[0112] Over-etching and damage to the tool can be more effectively prevented by observing spectral data including punch-through events. Additionally, based on this spectral data, the cleaning process can be adjusted (e.g., after one of these punch-through events), for example, according to the method described in Figure 4A or 4B, to reduce damage caused by over-cleaning.

[0113] Figure 6 is a flowchart showing a method 600 for detecting a clean endpoint according to some embodiments. One or more functions of the method 600 can be performed or caused to be performed by a computerized device or system. The structure for performing Figure 6 the functions depicted in one or more of the steps shown includes hardware and / or software components of such a computerized device or system, such as a controller device, a computerized system, or a computer-readable device, which includes a storage medium storing computer-readable and / or computer-executable instructions that are configured to cause at least one processor device or computerized device to perform these operations when executed by the processor device. The controller can be an example of a computerized device or system. A subsystem (e.g., 191) can be another example of a computerized device or system. A processing chamber can be another example of a computerized device or system. Exemplary components of the processing chamber and the controller are depicted in Figure 1A and Figure 1B and Figure 13 and are described in more detail elsewhere herein.

[0114] It should also be noted that the operations of the method 600 can be performed in any suitable order, not necessarily the Figure 6 order depicted. Additionally, the method 600 can include more or fewer operations than those depicted in Figure 6 to detect a clean endpoint.

[0115] In some embodiments, the computerized device or system can include at least one spectral sensor of a semiconductor device manufacturing apparatus.

[0116] At block 602, method 600 may include introducing a chamber cleaning substance into the processing chamber to remove a coating from one or more components of the processing chamber without exciting or generating a plasma within the processing chamber. Generally, excitation is caused by applying electrical energy to a processing gas within the processing chamber or other manufacturing equipment. For example, the electrical energy may be applied using an inductive or capacitive method. Excitation may cause at least a portion of the gas to enter an electrically excited state. In some embodiments, the chamber cleaning substance introduced into the processing chamber may include a reactive gas (e.g., radical fluorine) to produce silicon tetrafluoride (SiF4), which may then be removed from the chamber. In certain cases, the coating may be a byproduct material from a deposition process, including gaseous substances (e.g., nitrides, oxides, carbon) presented in the form of a film that has been deposited onto various surfaces of the processing chamber.

[0117] At block 604, method 600 may include using at least one sensor to detect spectral characteristics of electromagnetic radiation emitted within the processing chamber during chamber cleaning. In some embodiments, the sensor may be a spectral sensor of the type described having Figure 2A - 2B the described functionality and use, including a spectral sensor capable of acquiring spectral information and capable of generating spectral data (such as Figure 2A - 2B those shown in Figure 3 and 5 . In some embodiments, the spectral characteristics may include a spectrum or spectral pattern, which may include frequency bands, each of which may have one or more aspects (e.g., intensity, brightness, thickness) related to chamber conditions, such as the presence of a film on a surface within the processing chamber.

[0118] Electromagnetic radiation in the context of the present disclosure may refer to ultraviolet (UV), infrared (IR), and / or visible light. In certain cases, the electromagnetic radiation may be generated by a reaction or process within the processing chamber, such as chemiluminescence from a complex chemical reaction. Such a process may correspond to certain spectral patterns and may be compared, for example, to a reference library of lines or reference spectral patterns or frequency bands, as described above.

[0119] At block 606, method 600 may include determining that at least a portion of the electromagnetic radiation emitted within the processing chamber is caused by a chemical reaction of the chamber cleaning substance with the coating or with at least one of the one or more components of the processing chamber based on the spectral characteristics. Since the chemical reaction with the coating is different from the chemical reaction with the components of the processing chamber (e.g., chamber walls made of aluminum), the spectral characteristics will also be different. For example, different spectra or spectral patterns may be obtained based on the detected electromagnetic radiation (e.g., at block 604). These spectra or patterns may be known and thus it may be determined from the spectral characteristics that the electromagnetic radiation is caused by a chemical reaction with the coating or with the components of the processing chamber.

[0120] In some embodiments, these spectral characteristics can include band intensities associated with spectral patterns. For example, a wider or brighter band in the spectral pattern representing the coating can indicate the presence of the coating, while a thinner or darker band can indicate less coating. Similarly, these bands can indicate the degree of exposure of chamber components (e.g., aluminum surfaces).

[0121] Accordingly, method 600 can also include determining whether chamber cleaning of one or more components has been completed based on an aspect of the spectral characteristics (e.g., intensity, brightness, width). In some implementations, method 600 can also include adjusting and / or stopping one or more processes associated with the processing chamber based on this determination (e.g., to reduce damage caused by over-cleaning).

[0122] Figure 7 is a flowchart depicting another method 700 for detecting a cleaning endpoint according to some embodiments. One or more of the functions of method 700 can be performed or caused to be performed by a computerized device or system. The structure for performing Figure 7 the functions shown in one or more of the steps shown can include hardware and / or software components of such a computerized device or system, such as a controller device, a computerized system, or a computer-readable device, which includes a storage medium storing computer-readable and / or computer-executable instructions that are configured to cause at least one processor device or computerized device to perform operations when executed by a processor device. A controller can be an example of a computerized device or system. A subsystem (e.g., 191) can be an example of a computerized device or system. A processing chamber can be another example of a computerized device or system. Exemplary components of the processing chamber and the controller are shown in Figure 1A and Figure 1B and Figure 13 and are described in more detail elsewhere herein.

[0123] It should also be noted that the operations of method 700 can be performed in any suitable order, not necessarily the Figure 7 order depicted. Additionally, method 700 can include more or fewer operations than the Figure 7 operations depicted in order to detect a cleaning endpoint.

[0124] In some embodiments, the computerized device or system can include at least one spectral sensor of a semiconductor device manufacturing apparatus.

[0125] At block 702, method 700 may include introducing a chamber cleaning substance into the processing chamber to remove a coating from one or more components of the processing chamber without exciting or generating a plasma within the processing chamber. In some embodiments, the chamber cleaning substance introduced into the processing chamber may include a reactive gas (e.g., radical fluorine) to generate silicon tetrafluoride (SiF4), which may then be removed from the chamber. In certain cases, the coating may be a byproduct material from a deposition process, including gaseous substances (e.g., nitrides, oxides, carbon) presented in the form of a film that has been deposited onto various surfaces of the processing chamber.

[0126] At block 704, method 700 may include generating a reference plasma. The reference plasma is independent of any manufacturing processes that may occur within the processing chamber. In some embodiments, the reference plasma may be a helium plasma. In other embodiments, the reference plasma may be a neon- or argon-based plasma, a hydrogen plasma, or other types of plasma.

[0127] At block 706, method 700 may include detecting spectral characteristics of electromagnetic radiation emitted by one or more substances excited by the reference plasma in the processing chamber. In some embodiments, the detection may be accomplished by using a sensor, such as a spectral sensor of the type Figure 2A - 2B described and having the functions and uses Figure 2A - 2B described, including a spectral sensor capable of acquiring spectral information and capable of generating spectral data (such as Figure 3 and 5 those shown).

[0128] At block 708, method 700 may include determining that a coating has been removed from at least one of one or more components of a processing chamber based on spectral characteristics. In some embodiments, the spectral characteristics may include a spectrum or spectral pattern, which may include frequency bands, each of which may have one or more aspects (e.g., intensity, brightness, width) related to chamber conditions, such as the presence of a film on a surface in the processing chamber. Thus, in some methods, it may be determined that a coating has been removed from at least one of one or more components of a processing chamber by: (i) comparing the spectral characteristics to a reference library of spectral lines or spectral patterns or frequency bands, and (ii) determining whether the spectral characteristics match the reference library (e.g., a reference spectrum). For example, if the detected spectral pattern corresponds to a spectral pattern associated with certain aluminum and / or certain coating materials, it may be determined that the coating has been removed from the chamber walls. As another example, if the detected spectral pattern corresponds to a spectral pattern associated with certain materials (excluding aluminum) of the coating, it may be determined that the coating has not been removed from the chamber walls. In some cases, a partial match may be sufficient (e.g., only some spectral patterns or frequency bands correspond to the reference spectral pattern or frequency bands).

[0129] If it is determined that a coating has been removed from at least one of one or more components of a processing chamber, that determination will correspond to a chamber cleaning endpoint. Thus, in some implementations, method 700 may also include adjusting and / or stopping one or more processes associated with the processing chamber based on that determination (e.g., to reduce damage caused by over-cleaning).

[0130] Thus, methods 600 and 700 are methods of using spectral information to clean a chamber without using methods that cause tool degradation, such as overly conservative timed cleaning or expensive IR-EPD. Hazard Removal

[0131] In another exemplary aspect and application of spectral sensing in accordance with the embodiments described herein, spectral sensing (e.g., OES) may be used to detect the degree of contamination from harmful materials in or around a manufacturing tool.

[0132] Related to the chamber cleaning described above, such cleaning poses a health hazard to the operator of the tool being cleaned. Chamber cleaning may cause harmful substances (e.g., fluorine-containing substances or other substances harmful to health and / or chemically corrosive) to adsorb onto the surfaces of the entire tool, thereby posing a health hazard to the operator when the chamber is opened (during chamber opening activities). For example, water vapor may enter the chamber, react with the fluorine-containing substances to produce hydrogen fluoride (HF), and escape from the chamber, posing a toxic hazard to the operator of the tool. Due to such safety hazards, harmful materials must be removed before opening the chamber.

[0133] In some cases, this hazard can be mitigated by using a series of HF purge cycles, venting the chamber, and pumping it down again. The number of purge cycles is fixed and can be determined based on historical measurements using chemical sensors. However, the actual number of cycles required depends on the tool history, which can be related to, for example, the type of film deposited and / or the level of film accumulation on the walls or the surface mechanical properties based on age and condition (porous or deteriorated surfaces may retain more harmful substances). Therefore, typically, the number of cycles is conservatively set to a large number to ensure that there are no harmful substances when the chamber is opened. This significantly increases tool downtime and reduces wafer throughput.

[0134] In some embodiments of the methods described herein, spectral sensing (e.g., using an OES sensor) can be used to evaluate the level of contaminants (e.g., fluorine or other harmful substances) in the chamber before opening the chamber. During the pump / purge cycle or during an alternative HF purge process, a reference plasma may be ignited inside the tool. The reference plasma can then be monitored for direct or indirect spectral characteristics of HF.

[0135] Similar to clean endpoint processing, even if the emissive species is not a product of interaction with the wall, changes in the spectral characteristics of the plasma can still indicate the surface condition inside the tool. The chamber conditions can be a function of HF and / or other fluorine-containing substances absorbed on the chamber walls or other chamber surfaces or components. The absorbed substances affect the plasma, and the plasma is detectable in spectral regions that are strongly sensitive to chamber conditions, including the fluorine-containing substances absorbed on the chamber surfaces.

[0136] Figure 8A FIG. 800 is an exemplary spectrogram that indicates peaks corresponding to contamination caused by harmful substances (e.g., fluorine) after multiple purge cycles. As shown, the spectral peaks occur at different key wavelengths. For example, within certain bands (the 379 - 388 nm band is an exemplary band), the spectral peaks may be particularly sensitive to chamber conditions (e.g., the presence of HF). It can be seen that the peak 802 within band 803 (e.g., within the 379 - 388 nm band) becomes lower with the purge cycles. In some cases, the reduction in the peak may decrease with the purge cycles. After approximately purge cycle 5 (the peak indicated by the dashed oval 804), the HF-related spectral signal measured using the spectral sensor can remain relatively the same irradiance; the peak may not decrease as much. This can indicate an acceptable safety level, which in some implementations can be determined based on an HF-related spectral signal that varies no more than a threshold range or threshold amount, or is below a threshold amount.

[0137] Notably, in some cases, only certain peaks or frequency bands are highly sensitive to the chamber state. In the exemplary spectrogram 800, the peak 802 within the frequency band 803 is the most sensitive to the chamber state.

[0138] Figure 8B Exemplary graph 820 indicates contamination-related signals (e.g., caused by the presence of HF) during multiple purge cycles. Around and after purge cycle 6, the HF-related spectral signal remains relatively constant, corresponding to the exemplary spectrogram 800. In some embodiments, it can be determined that the contamination is at a sufficiently low and acceptable level based on a threshold (e.g., threshold line 822). In some embodiments, it can be determined that the contamination is at a sufficiently low and acceptable level based on a signal that varies no more than a threshold amount or range 824.

[0139] Using spectral data as depicted in Figure 6 A and 6B, the number of purge cycles can be optimized. Instead of conservatively setting the number of purge cycles to a large preset number, the spectral data can indicate that the contamination is low enough or has stabilized before reaching a preset number of purge cycles. In one example, it can be determined that based on Figure 8A and 8B the data shown, the purge cycle can stop after the sixth cycle because it has dropped below the threshold signal level (e.g., below the threshold line 822). In some implementations, the threshold signal level can be determined based on an acceptable HF safety level or even lower than that acceptable safety level (e.g., below one or more standard deviations). In addition to the above example, after confirming or verifying that subsequent readings remain within the maximum variation of the HF-related signal level and / or below the threshold signal level, the purge cycle can stop after the sixth cycle (e.g., at the seventh or eighth cycle). This additional attention can ensure minimizing the risk to the operator. Nevertheless, safely stopping the purge cycle before that conservative number can beneficially reduce tool downtime, thereby increasing wafer yield compared to typical methods by eliminating unnecessary purge cycles.

[0140] Figure 9 is a flowchart depicting another method 900 for determining the optimal number of purge cycles for semiconductor device manufacturing equipment according to some embodiments. One or more of the functions of the method 900 can be performed or caused by a computerized device or system. For performing Figure 9The structure of the functionality shown in one or more of the steps shown may include hardware and / or software components of such a computerized device or system, such as a controller device, a computerized system, or a computer-readable device, which includes a storage medium storing computer-readable and / or computer-executable instructions that are configured to cause at least one processor device or computerized device to perform operations when executed by a processor device. A controller can be an example of a computerized device or system. A subsystem (e.g., 191) can be another example of a computerized device or system. A processing chamber can be another example of a computerized device or system. Exemplary components of the processing chamber and the controller are shown respectively in Figure 1A and Figure 1B and Figure 13 and are described in more detail elsewhere herein.

[0141] It should also be noted that the operations of method 900 can be performed in any suitable order, not necessarily the Figure 9 order depicted. Additionally, method 900 can include more or fewer operations than those Figure 9 depicted to determine the optimal number of cleaning cycles.

[0142] In some embodiments, the computerized device or system can include at least one spectral sensor of a semiconductor device manufacturing apparatus.

[0143] At block 902, method 900 can include exposing the processing chamber to a hazardous material. In some embodiments, the hazardous material can include fluorine and / or fluorine-containing substances (e.g., hydrogen fluoride (HF)) or other substances that are harmful to health and / or chemically aggressive. The introduction of such a hazardous material (e.g., HF) can be a result of a process that is not necessarily a deposition or other yield-related process, but is related to what happens before or after a deposition or other yield-related process. For example, chamber cleaning can cause hazardous substances (e.g., fluorine-containing substances) to adsorb to the surfaces of the entire tool, posing a health hazard to operators during chamber opening events.

[0144] At block 904, method 900 can include cleaning the processing chamber one or more times. In some embodiments, the processing chamber can be cleaned multiple times. In some implementations, the cleaning can include HF cleaning cycles. In certain cases, the number of times the processing chamber is cleaned can be less than the number of times known to result in safe chamber conditions (e.g., the absence of hazardous substances). For example, it is known that 10 cleanings will result in an acceptably low level of HF in the processing chamber, so the processing chamber can be cleaned fewer than 10 times, such as between 4 and 7 times, or such that the lower number of cleanings may or may not result in hazardous substances remaining in the processing chamber.

[0145] At block 906, method 900 may include generating a reference plasma in a processing chamber.

[0146] At block 908, method 900 may include detecting spectral characteristics of electromagnetic radiation emitted by a material excited by the reference plasma in the processing chamber. In some embodiments, these spectral characteristics may be detected by a spectral sensor of the type described with respect to Figure 2A - 2B In some embodiments, the spectral characteristics may include a spectrum or spectral pattern, which may include frequency bands, each of which may have one or more aspects (e.g., intensity, brightness, width) related to chamber conditions (such as the presence of a hazardous material in the processing chamber).

[0147] For example, the spectral characteristics may be related to the presence of fluorine-containing substances in the processing chamber, or may be related to the state of the processing chamber associated with the fluorine-containing substances.

[0148] At block 910, method 900 may include determining that there is no fluorine-containing substance in the processing chamber based on the spectral characteristics. In some methods, determining that there is no fluorine-containing substance in the processing chamber may be performed by: (i) comparing the spectral characteristics with a reference library of spectral lines or spectral bands, and (ii) determining whether the spectral characteristics match the reference spectrum. For example, if the comparison result shows that the spectral characteristics of the fluorine-containing substance do not match the reference spectrum, it may be determined that the fluorine-containing substance does not exist. On the other hand, if the comparison result does show a match, it may be determined that the fluorine-containing substance still exists in the processing chamber.

[0149] At block 912, method 900 may include not performing a further cleaning of the processing chamber based on determining that there is no fluorine-containing substance in the processing chamber. This may correspond to, for example, Figure 8B the cleaning number 6 and subsequent numbers in Figure 8B In some implementations, not performing a further cleaning may be further based on an indication of an acceptable safety level, which is determined based on the change in the HF-related spectral signal not exceeding a threshold range or amount (e.g., Figure 8B 824 of

[0150] In some embodiments, determining that the fluorine-containing substance does not exist in the processing chamber may include performing a further cleaning, i.e., a "safety cleaning". Although this may result in a more conservative number of cleaning cycles, this additional caution can ensure minimizing the risk to the operator.

[0151] In some embodiments, method 900 may include performing one or more additional cleanings of the processing chamber based on determining that a fluorine-containing substance still exists in the processing chamber. This may correspond to, for example, Figure 8BThe number of cleanings is 3 or 4. In some embodiments, further cleaning of the processing chamber can cause method 900 to return to block 904 to clean the processing chamber one or more times, generate a reference plasma, and determine if fluorine-containing materials are present. In some embodiments, when the processing chamber is cleaned one or more times during these subsequent cleaning cycles, the number of cleaning cycles can be reduced compared to the initial number of cleaning cycles, for example, reduced to one cleaning cycle.

[0152] Since the purpose of using the spectral signal is to determine the optimal number of cleaning cycles, reducing subsequent cycles can prevent the cycles from being performed an unnecessary number of times. However, the optimal number of cleaning cycles can also be balanced with the RF power required to generate the reference plasma (block 806), because it may not be known how many more cleaning cycles must be performed to reach an acceptable safety level for hazardous materials. Thus, in some implementations, the reduction in the number of cleaning cycles can be gradual rather than reduced to the minimum number 1. Trace Gas and Leak Detection

[0153] In another exemplary aspect and application of spectral sensing according to the embodiments described herein, spectral sensing (e.g., OES) can be used to detect trace gases in a manufacturing tool. One issue with trace gases is the presence of air leaks, valve leaks, or other unexpected gas eruptions during a processing step. Some deposition processes are very sensitive to trace contaminants.

[0154] Conventional methods do not provide a real-time response. Generally, air leaks can be identified by a rising rate measurement (for large leaks) or with the aid of a leak detector (e.g., a helium leak detector, a mass spectrometer) (for small leaks). However, rising rate measurements take a long time, especially for small leaks, and lack precision. Also, leak detectors are cumbersome, requiring breaking the vacuum (e.g., the vacuum in the processing chamber or foreline), and their availability is often limited. Mass spectrometers are generally used to identify trace gases other than air, such as impurities in a process gas or outgassing from a surface. Mass spectrometers have similar drawbacks to leak detectors and are generally not suitable for reactive chemicals, such as those in a deposition apparatus. More specifically, mass spectrometers may be limited to specific substances, expensive, invasive (e.g., requiring integration in a vacuum system), or vulnerable to contamination from chemically harsh environments.

[0155] Conversely, spectral sensing and measurement can be used in accordance with a spectral sensor (e.g., an OES sensor) and the embodiments disclosed herein (without using a mass spectrometer or performing a length rise rate measurement). Different from the commonly used devices described above, the spectral sensor can be configured to sense many different substances, with lower cost and without affecting throughput. In some embodiments, spectral features related to the pollutant under discussion can be measured. For example, measuring the integrated intensity of a spectral band of a certain substance (e.g., a nitrogen band) can be used as a means to evaluate whether there is an air leak. This can be accomplished by igniting a plasma in a continuous gas flow, then closing the pump valve and shutting off the gas flow. By tracking the integrated intensity of the nitrogen band over time, a person of ordinary skill in the art of the present invention can infer the leak rate by normalizing to a reference line (e.g., an argon transition) and an initial calibration measurement (e.g., a rise rate).

[0156] Figure 10A Exemplary spectrogram 1000 indicates the presence of different gas species. In some methods, a plasma (containing, for example, helium) can be ignited in a processing chamber at time = 0, where the processing chamber has shut off the vacuum pump and all valves leading to the gas source to create a stagnant volume. Plasma ignition can generate electromagnetic radiation (e.g., light) from any gas species that dissociates and is then excited in the plasma. Based on spectral data collected over time (here, 100 seconds) after plasma ignition (e.g., using a spectral sensor), exemplary spectrogram 1000 can be generated. Certain spectral lines or bands can be indicated in exemplary spectrogram 1000, where the intensity is highest near level 1008 and lower near level 1010. Spectral lines 1002 and 1004 on exemplary spectrogram 1000 can have an intensity of approximately level 1008 and correspond to helium. Spectral line 1006 can have an intensity of approximately level 1010 and correspond to atomic oxygen. Since the valves were closed and the gas flow stopped at time = 0, any detected gas presence can indicate a leak. In this case, any detected oxygen (based on spectral line 1006, although weak) can be inferred to be, for example, a leak from the walls or through a leak in a gas valve or pipeline or seal.

[0157] In this case, since the amount of oxygen in the system is low, the plasma will not be significantly altered. The plasma remains more or less unchanged. However, by measuring the change in the signal (e.g., the oxygen signal) intensity over time (e.g., 100 seconds or longer), a graph 1020 as shown can be obtained. Figure 10B Line 1022 indicates a relatively consistent increase in the light intensity from the oxygen signal.

[0158] In an alternative method, rather than igniting the plasma, a pressure sensor (e.g., a manometer) can be used to measure the rise in oxygen, and a similar indication of the oxygen rise can be obtained from the line 1024 that measures the gas pressure. However, this type of measurement takes a long time (e.g., several hours) because the leak in this case is small. In contrast, using spectroscopic measurements according to the above method can obtain similar measurement results and indications within a shorter time period (e.g., a few minutes or as short as a few seconds). That is, a spectroscopic sensor can be used to obtain an indication of a very sensitive (e.g., small leak) measurement of air or gas leakage in a manufacturing system or its components, without having to resort to larger and more expensive equipment whose availability may be limited. Limited amplitude signal detection

[0159] In some processes, the spectroscopic signal emitted by the substance under consideration in the processing chamber may be weak (e.g., below the reliable detection limit of the spectroscopic sensor or other predetermined limit). Examples of the reasons for weak spectroscopic (e.g., OES) signals include (i) when a spectroscopic signal is required (e.g., HF sweep), there is no plasma in the chamber; and (ii) the local plasma power is insufficient to sufficiently excite the substance under consideration. An example of a system that produces a local weak plasma is a system that employs remote plasma, such as the system used in some chamber cleaning operations. Such limited amplitude optical frequency signals can be addressed by one or more of the following techniques.

[0160] In some cases, some spectroscopic signals can be obtained from chemiluminescence, or other light emitted by a chemical reaction, such as light emitted by the reaction of fluorine or fluorine-containing substances with the material on the chamber wall. This may occur during the reaction of fluorinated substances during chamber cleaning.

[0161] In some cases, a reference plasma can be generated, where the reference plasma is a plasma that is not required for the process under consideration (e.g., a plasma not required for etching, deposition, or cleaning processes), or a plasma generated mainly or specifically for the purpose of facilitating spectroscopic or OES detection. The reference plasma can excite the substance of interest, and the emission spectrum of the emitted substance can be detected by a spectroscopic sensor. For example, HF can be detected by the spectroscopic signal of HF excited by the reference plasma.

[0162] Figure 11A and Figure 11B are flowcharts depicting methods 1100 and 1150 for detecting limited amplitude spectroscopic signals in a processing chamber of a semiconductor device manufacturing apparatus according to some embodiments. One or more of the functions of methods 1100 and 1150 can be performed or caused by a computerized device or system. For performing Figure 11A and Figure 11BThe structure of the functionality shown in one or more of the steps shown may include hardware and / or software components of such a computerized device or system, such as a controller device, a computerized system, or a computer-readable device, which includes a storage medium storing computer-readable and / or computer-executable instructions that are configured to cause at least one processor device or computerized device to perform operations when executed by the processor device. The 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 processing chamber may be another example of a computerized device or system. Exemplary components of the processing chamber and the controller are shown respectively in Figure 1A and Figure 1B and Figure 13 and are described in more detail elsewhere herein.

[0163] It should also be noted that the operations of methods 1100 and 1150 may be performed in any suitable order, not necessarily the Figure 11A and Figure 11B depicted order. Additionally, methods 1100 and 1150 may include more or fewer operations than the Figure 11A and Figure 11B depicted operations to detect a limited spectral signal.

[0164] In some embodiments, the computerized device or system may include at least one spectral sensor of a semiconductor device manufacturing apparatus.

[0165] Referring to Figure 11A , at block 1102, method 1100 may include generating a reference plasma in a processing chamber containing a first chemical used in a process lacking a plasma. The reference plasma is not a plasma associated with a manufacturing process. In some embodiments, the reference plasma may be a helium plasma. In other embodiments, the reference plasma may be a neon- or argon-based plasma, a hydrogen plasma, or other types of plasma. In certain cases, the first chemical may be a gas, such as oxygen. In certain cases, even when all valves of the vacuum pump and gas source are closed, there may still be trace amounts of gas present in the processing chamber due to leaks.

[0166] At block 1104, method 1100 may include detecting spectral characteristics of light emitted by one or more substances excited by the reference plasma in the processing chamber. In some embodiments, the detection may be accomplished by using a sensor, such as a spectral sensor of the type described with respect to Figure 2A - 2B and having the functionality and uses described with respect to Figure 2A - 2B , including being able to acquire spectral information and being able to generate spectral data (such as Figure 3 ,10A a spectral sensor as shown in FIGS. 10A and 10B. In some embodiments, the spectral characteristics may include a spectrum or spectral pattern, which may include frequency bands, and each frequency band may have one or more aspects (e.g., intensity, brightness, width) related to the chamber conditions, such as trace gases in the processing chamber caused by gas leakage.

[0167] At block 1106, method 1100 may include determining that a first chemical substance is present in the processing chamber based on the spectral characteristics. In some embodiments, determining the presence of the first chemical substance may be accomplished by: (i) comparing the spectral characteristics with a reference library of spectral lines or spectral bands, and (ii) determining whether the spectral characteristics match a reference spectrum. For example, if the comparison results disclose that the spectral characteristics of the first chemical substance do not match the reference spectrum, it may be determined that the first chemical substance is not present. On the other hand, if the comparison results do show a match, it may be determined that the first chemical substance is present in the processing chamber. In some cases, the intensity value of the spectral characteristics may be considered. If the intensity value from the detected spectral characteristics has not reached a threshold level (e.g., if the oxygen signal has not reached Figure 10A level 1010 or some other predetermined value), it may not be considered a match with the reference spectrum. As Figure 10A and 10B shown, even trace amounts of the first chemical substance can be detected, especially over a period of time (e.g., 100 seconds or longer).

[0168] In some embodiments, the methods 400 or 450 described with respect to Figure 4A and Figure 4B can be used to detect trace gas leaks and adjust the processing. For example, based on detecting a gas leak using spectral data obtained via a spectral sensor, the configuration of the manufacturing tool can be changed (including, for example, repairing components such as leaky gas valves or gas lines).

[0169] Referring to Figure 11B , which shows a flowchart of a more general method 1150 for illustrating the detection of a limited amplitude spectral signal in a processing chamber. At block 1152, method 1150 may include detecting spectral characteristics of light emitted in the processing chamber in a processing chamber having weak free gas. In some embodiments, a spectral sensor (e.g., an OES sensor) may detect and identify the components of the emitted light at specific wavelengths and intensities (e.g., Figure 3 the exemplary spectrum shown in

[0170] At block 1154, method 1150 may include determining that at least a portion of the light emitted in the processing chamber is caused by a chemical reaction in the processing chamber based on the detected spectral characteristics. In some cases, the light may be generated by chemiluminescence of a chemical reaction (e.g., a recombination chemical reaction). In some cases, the light may be generated by exciting a gas species with a plasma in the processing chamber.

[0171] In some cases, due to low spectral signals caused by, for example, few or weak reactions in the processing chamber, the intensity of spectral lines may be very weak. For example, it may be difficult to detect low-light chemiluminescence using a camera. To this end, in some embodiments, determining that at least a portion of the emitted light is caused by a chemical reaction may be based on: an evaluation of the spectral characteristics relative to a reference library of known spectral lines and / or spectra of the chemical reaction of interest. For example, the components of the emitted light at a particular wavelength and intensity (detected and identified using, for example, a spectral sensor) may be compared to the components of the reference library to find a match indicating that the emitted light corresponds to a chemical reaction that is known to produce substantially similar components. Spectral characteristics such as spectral bands may be said to be substantially similar if the associated wavelengths are close within a margin (e.g., within the spectrometer resolution, e.g., within about 2 nm) and / or the brightness of the detected spectral band and the reference spectral band falls within a certain range.

[0172] In some embodiments, method 1150 may further include performing at least one operation on the processing chamber based on determining that at least a portion of the emitted light is caused by a chemical reaction. Some examples of the operations performed include process diagnostics, process control, chamber clean endpoint detection, hazard scavenging (e.g., determining when to stop a scavenging cycle for HF contamination), and other applications discussed herein. Indirect Detection of Chamber State

[0173] Regarding indirect detection of spectral characteristics, the chamber condition or chamber state is not simply the presence or concentration of a particular chemical species that produces a particular spectrum when excited in the chamber. In other words, the chamber condition is not simply defined by the presence or absence of chemical species. The chamber condition may be a characteristic of the chamber itself to be monitored.

[0174] One example is the presence (or absence) of a specific coating or substance on the chamber wall. A wall coated with a dielectric material (e.g., a wall that needs to be cleaned) produces a different plasma response (and thus reflects a different plasma state) than a metal (e.g., uncoated or clean) wall. Chamber conditions can affect the plasma state, and this plasma state can affect at least a portion of the spectral signal. Thus, chamber conditions can be indirectly determined based on the spectral signal generated by the substances in the chamber. Another example is an adsorbed substance, such as fluorine or hydrogen fluoride (e.g., fluorine substances remaining from chamber cleaning). Another example is a gas leak in the process chamber, including a micro-leak.

[0175] The mechanism for using spectral information to characterize the chamber state or conditions can involve some spectral peaks of certain chemical substances that are very sensitive to the general plasma conditions that vary according to the chamber state or conditions. Some methods for characterizing the chamber state can involve generating a reference plasma and analyzing the resulting spectral signal; the magnitudes of certain peaks of certain chemical substances in the chamber depend largely on the state of the process chamber.

[0176] The relationship between the chamber state or conditions and the OES sequence can follow the following indirect path:

[0177] Chamber state: The chamber state or chamber conditions are the characteristics of the chamber to be determined. One example of the chamber state or chamber conditions is the presence of a coating or substance on the chamber wall or pedestal. A wall coated with a dielectric material (e.g., a wall that needs to be cleaned) produces a different plasma response (and thus reflects a different plasma state) than a metal (e.g., uncoated or clean) wall. Another example is an adsorbed substance, such as fluorine or hydrogen fluoride (e.g., F substances left from chamber cleaning).

[0178] Chamber physical properties: Chamber physical properties are the physical properties of the chamber caused by the chamber state. In addition, the chamber physical properties may directly affect the plasma state; examples of the chamber physical state include chamber wall conductivity, chamber wall temperature, emissivity, secondary electron coefficient, and surface topography. It should be noted that in some applications, the chamber state and chamber physical properties are combined into one characteristic, such as the chamber physical property such as the conductivity of the chamber wall.

[0179] Plasma state: The plasma state is the characteristic of the plasma in the chamber caused by the chamber physical properties, and this plasma state has a strong influence on one or more parts of the emission spectra of one or more chemical substances in the chamber. Examples of the plasma state include plasma density, plasma potential, electron temperature, and degree of ionization.

[0180] Spectral signal or OES signal: One or more portions of the spectral signal are very sensitive to the plasma state and thus indirectly indicate the chamber state. In fact, a spectral detection scheme (e.g., using an OES sensor) can be designed to use specific peaks or other regions of the spectrum of one or more components in the processing chamber. The peak or other region can be selected because it exhibits strong sensitivity to the plasma state. Figure 8A An example showing the spectral signal sensitivity is presented, where only certain peaks or bands are highly sensitive to the chamber state.

[0181] The applications described above for spectral sensors can be generalized to determining the chamber conditions or chamber state in a processing chamber of a semiconductor device manufacturing apparatus. Some methods for characterizing the chamber conditions involve generating a reference plasma and analyzing the resulting spectral signal, and the magnitudes of certain peaks of certain chemicals in the chamber depend largely on the conditions or state of the processing chamber.

[0182] Figure 12 is a flowchart showing another method 1200 for indirectly determining the chamber conditions of a processing chamber of a semiconductor device manufacturing apparatus according to some embodiments. One or more of the functions of the method 1200 can be performed or caused by a computerized device or system. The structure for performing the functions shown in one or more of the steps shown can include hardware and / or software components of such a computerized device or system, such as a controller device, a computerized system, or a computer-readable device, which includes a storage medium storing computer-readable and / or computer-executable instructions configured to cause at least one processor device or computerized device to perform operations when executed by a processor device. A controller can be an example of a computerized device or system. A subsystem (e.g., 191) can be another example of a computerized device or system. A processing chamber can be another example of a computerized device or system. Exemplary components of the processing chamber and the controller are shown in Figure 12 and Figure 1A and Figure 1B and Figure 13 and are described in more detail elsewhere herein.

[0183] It should also be noted that the operations of method 1200 can be performed in any suitable order, not necessarily the Figure 12 order depicted. Additionally, method 1200 can include more or fewer operations than the Figure 12 operations depicted in to indirectly determine the chamber conditions.

[0184] In some embodiments, the computerized device or system can include at least one spectral sensor of a semiconductor device manufacturing apparatus.

[0185] At block 1202, method 1200 may include generating a plasma in a processing chamber that includes first chamber conditions. In some embodiments, the first chamber conditions may affect the plasma in a manner that renders the plasma in a first plasma state. Examples of the first chamber conditions include the presence (or absence) of a particular coating or substance on the chamber walls, adsorbed substances such as fluorine or hydrogen fluoride (e.g., residual fluorine substances from chamber cleaning), and gas leaks in the processing chamber, including minute leaks. In another example, the first chamber conditions may be associated with physical properties of the processing chamber, and the first plasma state is based on the physical properties, where the physical properties of the processing chamber may include the conductivity of the surface of the processing chamber, the temperature of the processing chamber, or a combination thereof. In some applications, the chamber conditions and chamber physical characteristics are combined into one feature, such as the conductivity of the chamber walls. In another example, the first plasma state may be plasma density, plasma potential, electron temperature, degree of ionization, or a combination thereof, where the first plasma state is based on the first chamber conditions.

[0186] At block 1204, method 1200 may include measuring a value of a light property at a first spectral feature of a substance in the processing chamber. In some embodiments, the light property may be the intensity of a spectral line or band, the intensity of a spectral signal at a particular wavelength, or a spectral pattern (e.g., the position of spectral lines or bands at certain wavelengths). In some embodiments, the light property may be based on the intensity of light emitted by a substance that reacts with the plasma in the processing chamber. In some embodiments, the first spectral feature is sensitive to the first plasma state. A spectral feature may be considered sensitive when it has a stronger response in intensity or other light property to the plasma state or chamber conditions than at least some other spectral features. Certain spectral peaks of certain chemical substances are very sensitive to general plasma conditions that vary according to chamber conditions.

[0187] At block 1206, method 1200 may include determining that the first chamber state exists in the processing chamber based on the value of the light property. The light property may indicate that the substance is causing or affecting the first chamber conditions of the processing chamber. For example, when a spectral band associated with oxygen is weak but its intensity increases over time, this may indicate a gas leak (see, e.g., Figure 10A and 10B ). As another example, when a spectral signal associated with chamber cleaning of a membrane weakens and experiences a breakthrough event (see, e.g., Figure 5 ), this may indicate the cleaning state of different parts of the processing chamber (e.g., walls, pedestal). In certain cases, the determination of the existence of the first chamber conditions in the processing chamber may be based on a value of the light property that meets or exceeds a threshold. For example, the light property (e.g., intensity) needs to be above a certain level to indicate the chamber conditions (e.g., chamber conditions of HF contamination).

[0188] In some cases, one or more portions of the spectral signal are highly sensitive to the plasma state and thus indirectly indicate chamber conditions. In fact, a spectral detection scheme can be designed to use specific peaks or other regions of the spectrum of one or more components in the processing chamber. The peak or other region can be selected because it exhibits strong sensitivity to chamber conditions or the plasma state. Apparatus - Computing and Controller Implementations

[0189] Figure 13 is a block diagram of an example of a computing device 1300 suitable for implementing some embodiments of the present disclosure. For example, device 1300 may be suitable for implementing some or all of the functions of the image analysis logic disclosed herein.

[0190] Computing device 1300 may include a bus 1302 that directly or indirectly couples the following devices: a memory 1304, one or more central processing units (CPUs) 1306, one or more graphics processing units (GPUs) 1308, a communication interface 1310, an input / output (I / O) port 1312, an input / output component 1314, a power supply 1316, and one or more presentation components 1318 (e.g., a display). In addition to CPU 1306 and GPU 1308, computing device 1300 may also include Figure 13 additional logic devices not shown, such as, but not limited to, an image signal processor (ISP), a digital signal processor (DSP), an ASIC, an FPGA, etc.

[0191] Although Figure 13 the various boxes are shown as being connected by lines via bus 1302, this is not intended to be limiting and is merely for clarity. 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., in addition to the memory of GPU 1308, CPU 1306, and / or other components, memory 1304 may also represent a storage device). In other words, Figure 13 the computing devices are merely illustrative. There is no distinction made between 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, as all of these are contemplated within the scope of Figure 13 the computing devices.

[0192] 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 other types of buses.

[0193] Memory 1304 may include any of a variety of computer-readable media. The computer-readable media may be any available media that can be accessed by computing device 1300. The computer-readable media may include volatile and non-volatile media, as well as removable and non-removable media. By way of example and not limitation, the computer-readable media may include computer storage media and / or communication media.

[0194] Computer storage media may include volatile and non-volatile media and / or removable and non-removable media implemented in any method or technology for storing 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., which represent 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 storage technology, CD-ROM, digital versatile disc (DVD) or other optical disc 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 itself include a signal.

[0195] Communication media may embody computer-readable instructions, data structures, program modules, and / or other data types in a modulated data signal, such as a carrier wave or other transmission mechanism, and includes 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 a direct-wire connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above items should also be included within the scope of computer-readable media.

[0196] The CPU 1306 can be configured to execute computer-readable instructions to control one or more components of the computing device 1300 to perform one or more of the methods and / or processes described herein. Each CPU 1306 can include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of simultaneously processing multiple software threads. The CPU 1306 can include any type of processor and can include different types of processors, depending on the type of computing device 1300 being implemented (e.g., a processor with fewer cores for a mobile device and a processor with more cores for a server). For example, depending on the type of computing device 1300, the processor can be an ARM processor implemented using reduced instruction set computing (RISC) or an x86 processor implemented using complex instruction set computing (CISC). In addition to one or more microprocessors or auxiliary coprocessors (such as a math coprocessor), the computing device 1300 can also include one or more CPUs 1306.

[0197] The GPU 1308 can be used by the computing device 1300 to render graphics (e.g., 3D graphics). The GPU 1308 can include many (e.g., dozens, hundreds, or thousands) of cores capable of simultaneously processing many software threads. The GPU 1308 can generate pixel data for an output image in response to a rendering command (e.g., a rendering command received from the CPU 1306 via a host interface). The GPU 1308 can include a graphics memory for storing pixel data, such as a display memory. The display memory can be included as part of the memory 1304. The GPU 1308 can include two or more GPUs operating in parallel (e.g., via a link). When combined, each GPU 1308 can generate pixel data for different parts of an output image or different output images (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU can contain its own memory or can share memory with other GPUs.

[0198] In an example where the computing device 1300 does not include the GPU 1308, the CPU 1306 can be used to render graphics.

[0199] The communication interface 1310 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 1300 to communicate with other computing devices via an electronic communication network, including wired and / or wireless communication. The communication interface 1310 may include components and functionality that enable communication over any of a variety of different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet), low-power wide area networks (e.g., LoRaWAN, SigFox, etc.), and / or the Internet.

[0200] The I / O port 1312 may enable the computing device 1300 to be logically coupled to other devices, including coupling to the I / O component 1314, the presentation component 1318, and / or other components, some of which may be built into (e.g., integrated in) the computing device 1300. Illustrative I / O components 1314 include microphones, mice, keyboards, joysticks, trackpads, satellite antennas, scanners, printers, wireless devices, etc. The I / O component 1314 may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by the 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, face recognition, biometric recognition, on-screen and near-screen gesture recognition, air gestures, head and eye tracking, and touch recognition (described in more detail below) related to the display of the computing device 1300. The computing device 1300 may include a depth camera, such as a stereoscopic camera system, an infrared camera system, an RGB camera system, touchscreen technology, and combinations thereof, for pose 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 the detection of motion. In some examples, the computing device 1300 may use the output of the accelerometer or gyroscope to render immersive augmented reality or virtual reality.

[0201] The power supply 1316 may include hardwired power, battery power, or a combination thereof. The power supply 1316 may provide power to the computing device 1300 to enable the components of the computing device 1300 to operate.

[0202] The presentation component 1318 may include a display (e.g., a monitor, touchscreen, television screen, head-up display (HUD), other display types, or a combination thereof), speakers, and / or other presentation components. The presentation component 1318 may receive data from other components (e.g., the GPU 1308, the CPU 1306, etc.) and output the data (e.g., as an image, video, sound, etc.).

[0203] 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 a particular task or implements a particular abstract data type. The present disclosure may be practiced in a variety of system configurations, including in handheld devices, consumer electronics, general - purpose computers, more specialized computing devices, etc. The present disclosure may also be practiced in a distributed computing environment where tasks are performed by remote processing devices linked through a communication network.

[0204] In some implementations, a “controller” (such as 190) is part of a system that includes various types of sensors as described herein. Such a system includes a manufacturing tool with a camera sensor. Such a system may include a semiconductor processing device that includes one or more processing tools, one or more chambers, one or more platforms for processing, and / or specific processing components (wafer pedestal, gas - flow system, etc.). These systems may be integrated with electronics to control their operation before, during, and after processing a semiconductor wafer substrate. The controller may utilize the analysis logic described above to implement or be coupled to the analysis logic. The controller may be implemented as logic, such as an electronic device with one or more integrated circuits, a memory device, and / or software that receives instructions, issues instructions, controls operations, and / or enables sensing operations.

[0205] An electronic device may be referred to as a “controller” that can control various components or sub - components of one or more systems. Depending on the processing requirements and / or system type, the controller may be programmed to control any of the processes disclosed herein, including the delivery of process gases, temperature settings (such as heating and / or cooling), pressure settings, vacuum settings, power settings, RF (radio frequency) generator settings in some systems, RF matching circuit settings, frequency settings, flow - rate settings, fluid delivery settings, position and operation settings, wafer transfer in and out of tools connected or docked to a specific system and other transfer tools and / or load locks.

[0206] Broadly speaking, a controller can be defined as an electronic device that has various integrated circuits, logic, memory, and / or software for receiving instructions, issuing instructions, controlling operations, enabling cleaning operations, enabling endpoint measurements, etc. The integrated circuits can include chips in the form of firmware that stores program instructions, digital signal processors (DSPs), chips defined as application-specific integrated circuits (ASICs), and / or one or more microprocessors or microcontrollers that execute program instructions (e.g., software). The program instructions can be instructions sent to the controller in the form of various individual settings (or program files), and the individual settings (or program files) define operation parameters for performing specific processing on or for a semiconductor wafer or system. In some embodiments, the operation parameters can be part of a recipe defined by a process engineer to complete one or more processing steps during the processing of one or more layers, materials, metals, oxides, silicon, silicon dioxide, surfaces, circuits, and / or die of a wafer.

[0207] The controller can be configured to control or cause the control of various components or sub-components of one or more systems. Depending on the processing requirements and / or system type, the controller can be programmed to control any processing that a manufacturing tool can use during a manufacturing operation, including adjusting or maintaining the delivery of process gases, 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 into and out of the manufacturing tool and / or load lock). Process gas parameters include process gas composition, flow rate, temperature, and / or pressure. Particularly relevant to the disclosed embodiments, the controller parameters can relate to plasma generator power, pulse rate, and / or RF frequency.

[0208] The processing parameters under the control of the controller can be provided in the form of a recipe and can be input using a user interface. Signals for monitoring the processing can be provided through the analog and / or digital input connections of the system controller. Signals for controlling the processing are output on the analog and digital output connections of the deposition device.

[0209] In one example, instructions for causing or sustaining a plasma are provided in the form of a process recipe. The associated process recipes may be arranged sequentially so that at least some of the process instructions may be executed simultaneously. In some implementations, instructions for setting one or more plasma parameters may be included in a recipe prior to a plasma ignition process. For example, a first recipe may include instructions for a first delay, instructions for setting the flow rate of an inert gas (e.g., helium) and / or a reactive gas, and instructions for setting a plasma generator to a first power set point. A second subsequent recipe may include instructions for a second time delay and instructions for enabling the plasma generator to supply 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 should be understood that within the scope of the present disclosure, these recipes may be further subdivided and / or repeated in any suitable manner. In some deposition processes, the duration of plasma triggering 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 implementations described herein, much shorter plasma excitations may be applied during a process cycle. Such plasma excitation durations may be on the order of less than about 50 milliseconds, and in a particular example, about 25 milliseconds is utilized. As explained, the plasma may be pulsed.

[0210] In some embodiments, a controller is configured to control and / or manage the operation of an RF signal generator. In certain implementations, the controller is configured to determine upper and / or lower thresholds of the RF signal power to be transmitted to a manufacturing tool, thereby determining the actual (e.g., real-time) level of the RF signal power transmitted to an integrated circuit manufacturing chamber, the RF signal power activation / deactivation times, the RF signal on / off durations, the duty cycle, the operating frequency, etc.

[0211] As a further example, the controller may be configured to control: the timing of various operations, the mixing of gases, the pressure in a manufacturing tool, the temperature in a manufacturing tool, the temperature of a substrate or pedestal, the position of a pedestal, chuck, and / or platen, and multiple cycles performed on one or more substrates.

[0212] The controller may include one or more programs or routines for controlling a design subsystem associated with a manufacturing 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 a processing tool component that loads a substrate onto a pedestal and controls the spacing between the substrate and other components of the manufacturing tool. The positioning program may include instructions for moving the substrate into and out of a reaction chamber to deposit a film on the substrate and clean the chamber.

[0213] The process gas control program may include program code for controlling gas composition and flow rate and for flowing gas into one or more processing stations prior to deposition to achieve pressure stabilization in the processing stations. In some implementations, the process gas control program includes instructions for introducing gas during film formation on a substrate in a reaction chamber. This may include introducing gas for one or more substrates in a batch of substrates for different numbers of cycles. The pressure control program may include program code for controlling the pressure in a processing station by adjusting, for example, a throttle valve in an exhaust system of the processing station, the gas flow rate into the processing station, etc. The pressure control program may include instructions for maintaining the same pressure during deposition for different numbers of cycles on one or more substrates during batch processing.

[0214] The heater control program may include program code for controlling the current flowing to a heating unit for heating a substrate. Alternatively, the heater control program may control the delivery of a heat transfer gas (e.g., helium) to the substrate.

[0215] In some implementations, there may be a user interface associated with the controller. The user interface may include a display screen, a graphical software display of the device and / or processing conditions, and user input devices such as a pointing device, a keyboard, a touch screen, a microphone, etc.

[0216] In some implementations, the controller can be part of or coupled to a computer that is integrated with, coupled to, networked to the system in other ways, or a combination thereof. For example, the controller can be in the "cloud" or be all or part of a fab host system, which can allow remote access to wafer processing. The computer can implement remote access to the system to monitor the current progress of processing operations, examine the history of past processing operations, examine trends or performance criteria of multiple processing operations, change parameters of the current processing, set processing steps to follow the current processing, or initiate a new processing. In some examples, a remote computer (such as a server) can provide a processing recipe to the system via a network (which can include a local network or the Internet). The remote computer can include a user interface that enables input or programming of parameters and / or settings, and then sends the parameters and / or settings from the remote computer to the system. In some examples, the controller receives instructions in the form of data that specify the parameters for each processing step to be performed during one or more operations. It should be understood that the parameters can be specific to the type of processing to be performed and the type of tool, and the controller is configured to interface with or control the tool. Thus, as described above, the controller can be distributed, for example, by including one or more discrete controllers networked together and working towards a common purpose (such as the processing and control described herein). An example of a distributed controller for such a purpose is one or more integrated circuits on a chamber in communication with one or more integrated circuits remote (such as at the platform level or as part of a remote computer), which combine to control processing on the chamber.

[0217] Exemplary systems can include, but are not limited to, plasma etch chambers or modules, deposition chambers or modules, spin rinse chambers or modules, metal plating chambers or modules, cleaning chambers or modules, bevel edge etch chambers or modules, physical vapor deposition (PVD) chambers or modules, chemical vapor deposition (CVD) chambers or modules, atomic layer deposition (ALD) chambers or modules, atomic layer etch (ALE) chambers or modules, ion implantation chambers or modules, track chambers or modules, and any other semiconductor processing system that can be associated with or used for the manufacture and / or preparation of semiconductor wafers.

[0218] System software can be organized in many different ways that can have different architectures. For example, various chamber component subroutines or control objects can be written to control the operation of chamber components necessary to perform deposition processing (and in some cases other processing) according to the disclosed embodiments.

[0219] As described above, depending on one or more processing steps to be performed by a tool, the controller may communicate with one or more other tool circuits or modules, other tool components, cluster tools, other tool interfaces, adjacent tools, neighboring tools, tools located throughout a factory, a host computer, another controller, or tools used in the material transport that shuttles a wafer container to and from tool locations and / or load ports in a semiconductor manufacturing factory.

[0220] Various adaptations to the implementations described in this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other implementations without departing from the spirit or scope of the disclosure. Accordingly, the claims are not intended to be limited to the implementations shown herein but are to be accorded the widest scope consistent with the disclosure, the principles disclosed herein, and the novel features.

[0221] Some features that are described in the context of separate implementations in this specification may also be implemented combinatorially in a single implementation. Conversely, the various features described in the context of a single implementation may also be implemented separately in multiple implementations or in any suitable sub-combination. Additionally, although features may be described above as acting in some combinations and even initially claimed as such, in some instances one or more features from a claimed combination may be deleted from the claimed combination, and the claimed combination may be directed to a sub-combination or a variation of a sub-combination.

[0222] Similarly, although operations are depicted in the figures in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in a sequential order, or that all of the operations shown be performed, to achieve the desired result. Additionally, the figures may schematically depict another exemplary process in the form of a flowchart. However, other operations not depicted may be incorporated into the exemplary process shown schematically. For example, one or more additional operations may be performed before, after, simultaneously with, or between any of the operations shown. In some cases, multitasking and parallel processing may be advantageous. Additionally, the separation of the various system components in the above implementations should not be construed as requiring such separation in all implementations, and it should be understood that the described program components and systems may generally be integrated together, in a single software product or packaged into multiple software products. Additionally, other implementations are within the scope of the appended claims. In some cases, the acts recited in the claims may be performed in a different order and still achieve the desired result.

Claims

1. A semiconductor device manufacturing apparatus, comprising: A processing chamber; At least one sensor having access to the processing chamber; and A controller communicatively coupled to the at least one sensor, the controller being configured to: (a) Introduce a chamber cleaning substance into the processing chamber to remove a coating from one or more components of the processing chamber without exciting or generating a plasma in the processing chamber; (b) During chamber cleaning, use the at least one sensor to detect spectral characteristics of electromagnetic radiation emitted in the processing chamber; And (c) Determine based on the spectral characteristics that at least a portion of the electromagnetic radiation emitted in the processing chamber is caused by a chemical reaction of the chamber cleaning substance with at least one of the coating or one or more components of the processing chamber.

2. The semiconductor device manufacturing apparatus according to claim 1, wherein, The chemical reaction includes a recombination reaction.

3. The semiconductor device manufacturing apparatus according to claim 1, wherein, The determining that at least a portion of the electromagnetic radiation is caused by the chemical reaction includes: evaluating a spectral pattern obtained using the at least one sensor against a reference spectral pattern.

4. The semiconductor device manufacturing apparatus according to claim 3, wherein, The controller is further configured to: determine a chamber cleaning endpoint based at least on determining that at least a portion of the electromagnetic radiation is caused by a chemical reaction with one or more components of the processing chamber; And Adjust one or more processes related to the processing chamber based on the determination of the chamber cleaning endpoint.

5. A method for detecting a cleaning endpoint, the method comprising: (a) Introduce a chamber cleaning substance into a processing chamber to remove a coating from one or more components of the processing chamber without exciting or generating a plasma in the processing chamber; (b) Generate a reference plasma; (c) Detect spectral characteristics of electromagnetic radiation emitted by one or more substances excited by the reference plasma in the processing chamber; And (d) Determine based on the spectral characteristics that the coating has been removed from at least one of the one or more components of the processing chamber.

6. The method according to claim 5, wherein: The spectral characteristics include one or more spectral patterns detected by a spectral sensor and having one or more aspects associated therewith; and The determining that the coating has been removed includes: (i) evaluating the one or more spectral patterns against a reference library of spectral bands, and (ii) determining whether the one or more aspects of the one or more spectral patterns at least partially match the reference library.

7. The method according to claim 6, further comprising: Determining the cleaning endpoint based at least on the determination that the coating has been removed; and Adjusting one or more processes related to the processing chamber based on the determination of the cleaning endpoint.

8. A semiconductor manufacturing apparatus, the apparatus comprising: A processing chamber; At least one sensor having access to the processing chamber; and A controller communicatively coupled to the at least one sensor, the controller being configured to: (a) Expose the processing chamber to fluorine and / or fluorine-containing substances; (b) Clean the processing chamber one or more times; (c) Generate a reference plasma in the processing chamber; (d) Detect the spectral characteristics of the electromagnetic radiation emitted by the substance excited by the reference plasma in the processing chamber; (e) Determine based on the spectral characteristics that there are no harmful substances in the processing chamber; and (f) Based on the determination that there are no harmful substances in the processing chamber, do not perform further cleaning on the processing chamber.

9. The semiconductor manufacturing apparatus according to claim 8, wherein, The harmful substance includes hydrogen fluoride.

10. The semiconductor manufacturing apparatus according to claim 8, wherein, The spectral characteristics are related to the presence of the fluorine-containing substance in the processing chamber or the state of the processing chamber related to the fluorine-containing substance.

11. A device for monitoring and controlling semiconductor device manufacturing equipment, the device comprising: at least one spectral sensor; and a controller communicatively coupled to the at least one sensor, the controller being configured to: use the at least one spectral sensor to detect the spectral characteristics of emissions from an internal part of the semiconductor device manufacturing equipment; and in response to determining, based on the detected spectral characteristics, that a desired processing condition within the internal part has not been achieved, adjust the processing related to the semiconductor device manufacturing equipment towards the desired processing condition.

12. The device according to claim 11, wherein The desired processing condition includes an end point of a chamber cleaning process, substantially no harmful substances within the internal part, or substantially no gaseous substances within the internal part.

13. A method for detecting a limited spectral signal in a processing chamber of semiconductor device manufacturing equipment, the method comprising: (a) Generate a reference plasma in a processing chamber, the processing chamber containing a first chemical substance used in a plasma-less process; (b) Detect the spectral characteristics of the light emitted by one or more substances excited by the reference plasma in the processing chamber; and (c) Determine based on the spectral characteristics that the first chemical substance is present in the processing chamber.

14. The method according to claim 13, wherein, The spectral characteristics of the light include one or more spectral bands obtained via a spectral sensor, and the determination that the first chemical substance is present in the processing chamber includes determining whether there is a match between the one or more spectral bands and a reference spectral band.

15. A method for indirectly determining the chamber state of a processing chamber, the method comprising: (a) Generate a plasma in a processing chamber having a first chamber state, wherein the first chamber state affects the plasma in such a way that the plasma exhibits a first plasma state; (b) Measure the value of the optical property at a first spectral feature of the substance in the processing chamber, wherein the first spectral feature is sensitive to the first plasma state; and (c) Determine based on the value of the optical property that the first chamber state is present in the processing chamber.

16. The method according to claim 15, wherein, The optical property includes the intensity of the light emitted by the substance reacting with the plasma in the processing chamber.

17. The method according to claim 15, wherein, The first chamber state includes whether there is a coating on the surface of the processing chamber, whether there is an adsorbed substance on the surface of the processing chamber, or a combination thereof.

18. The method according to claim 17, wherein The state of the first chamber is associated with the physical properties of the processing chamber, and the first plasma state is based on the physical properties; and wherein the physical properties of the processing chamber include the conductivity of the surface of the processing chamber, the temperature of the processing chamber, the secondary electron emission coefficient, or a combination thereof.

19. The method according to claim 17, wherein, The first plasma state includes plasma density, plasma potential, electron temperature, degree of ionization, or a combination thereof; and The first plasma state is based on the state of the first chamber.

20. The method according to claim 15, wherein The determination that the first chamber state exists in the processing chamber is based on the value of the optical property satisfying or exceeding a threshold.

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