System and method for monitoring a semiconductor process

Abstract

A system and method for monitoring a semiconductor process includes a plurality of sensors and a microcontroller. The plurality of sensors are disposed within a process chamber. The microcontroller receives data from the plurality of sensors and measures uniformity of the semiconductor process based on the data received from the plurality of sensors.

Description

Technical Field

[0001] Related applications

[0002] This application is a provisional application filed pursuant to the relevant parts of 35 USC 111 and 37 CFR 1.53. Background Technology

[0003] Deposition and etching processes are widely and universally used in semiconductor manufacturing plants during device fabrication in the semiconductor integrated circuit (IC) industry. The semiconductor industry's efforts to reduce dimensions—traditionally limited by the lithographic resolution of 2D structures—are shifting towards controlling the deposition and etching processes for 3D structures, such as 3D gates and 3D NAND. The ability to control the deposition and etching processes increasingly impacts device critical dimensions. That said, new technologies are needed to allow for more stringent process control during etching and deposition.

[0004] Plasma etching is often used to remove dielectric, semiconductor, or metal layers by using ignition gases in a plasma state (which drive the activation energy of a chemical reaction). Material removal can also be performed by flowing reactive gases (in a non-plasma state) or by using a wet etching (liquid-based) station. Film deposition on chamber components and treated substrates can be carried out using various methods, such as plasma-enhanced chemical vapor deposition (CVD), subatmospheric pressure CVD, thermal CVD, atomic layer deposition (ALD), plasma-enhanced atomic layer deposition, and so on. Depending on the process steps, etching and deposition processes can be isotropic or anisotropic (e.g., reactive ion etching - RIE).

[0005] In substrate deposition processes (such as IC manufacturing), many different layers can be deposited on a wafer (which is the substrate) through different reactions and various process material states. Example technologies include plasma (PECVD and high-density plasma-HDP), gas-sub-atmospheric pressure CVD (SACVD), and liquid (electroplating). Some examples of key parameters controlling the fabrication characteristics of deposited layers and devices are: thickness, stress, mass, resistivity, grain size, and refractive index. These parameters are measured and controlled not only for average values ​​(across the entire wafer or a batch of wafers) but also for wafer variability and inter-wafer variability. Reducing process variability helps improve yield at the end-of-line (EOL) process.

[0006] For example, substrate etching uses the following steps: a wafer etching step to apply a pattern (combined with a photolithography step) to the fabricated device; cleaning the wafer to remove contaminants; creating trenches between transistors; enabling separation between contacts and isolators; reacting the wafer surface prior to deposition; and removing photoresist. Key parameters controlling the etching process on the wafer include: critical dimensions of defined features, such as etching rate, thickness, stress, grain and defect control, and other electrical and optical parameters.

[0007] Substrate etching and deposition may or may not be performed in the same processing chamber, may be performed continuously in the chamber, may be performed non-sequentially in the chamber, or may be performed simultaneously in different chambers (e.g., in some HDP processes, etching and deposition may occur continuously or simultaneously).

[0008] During the substrate deposition sequence, byproducts from the process can be deposited onto the chamber components. This process can be non-uniform (different at different chamber locations), depending on the process steps or conditions. Chamber deposition occurs between substrate depositions when no substrate is processed inside the chamber. This deposition of coating chamber components (sometimes referred to as "undercoating," "pre-coating," "seasoning," etc.) results in enhanced wafer uniformity and control. Some examples of how substrate deposition affects the wafer process are: better grain performance on the wafer by "binding" the grains with an undercoating layer; better wafer process uniformity by applying a pre-coating to the chamber components; and improved control over the deposition rate by seasoning the chamber.

[0009] When there is no substrate inside the chamber, chamber etching occurs (between substrate etching / cleaning processes) to remove the coating from the chamber components and clean the chamber to remove byproducts. This process enhances particle properties and process control. Material is etched from the chamber components during this process.

[0010] Before or after the deposition step (on the wafer), some process steps may include pretreatment or post-treatment to etch the wafer surface. For example, pretreatment (before deposition) includes removing contaminants from the wafer surface to promote better adhesion of the deposited layer, and post-treatment to anneal or “shrink” the deposited layer. During those processes, byproducts removed from the substrate may adhere to and deposit on chamber components and / or etch different chamber components. For example, during plasma pretreatment of the substrate, byproducts may deposit on the chamber walls but are removed from the chamber chuck (where the wafer is placed).

[0011] Post-treatment maintenance deposition and etching (including preventative or reactive maintenance of the processing chamber, deposition and etching cycles with and / or without a substrate) will allow for better particle performance, process uniformity (within and between substrates), process control, and rate control. During this process, byproducts may be deposited on or removed from different chamber components (etching).

[0012] During wafer deposition and etching, cross impacts between different components within the chamber and along the substrate can arise from process parameters (e.g., flow rate, local temperature, plasma density, pressure, etc.) and vary depending on the conditions of the chamber components. These cross impacts affect both the conditions of the substrate and the chamber process (etching or deposition over the chamber components).

[0013] An example of the cross-effect between the chamber components and the treated substrate includes a substrate treated by a clean chamber (where no deposition occurred before the substrate entered). The deposition rate and thickness uniformity on the substrate may differ compared to a substrate treated in a chamber that already includes a coating material (e.g., a pre-conditioning layer).

[0014] Another example of cross-effects involves hard mask gate etching, where byproducts accumulated on the chamber walls affect the etching rate and critical size above the substrate.

[0015] Other forms of cross-influence include accumulated film on the chamber walls, where film stress builds up to the point of rupture, inducing particle shedding and wafer contamination. This adversely affects production yield and limits the accumulation window for wafer production. A plasma cleaning process must then be used to remove the residue from the processing chamber.

[0016] The terminology of local and direct process measurements using QCM refers to a method in which the mass added to or removed from the QCM surface alters the QCM frequency (described in the Sauerbery equation, which correlates the change in the oscillation frequency of the piezoelectric crystal with the mass deposited on its surface). Although the basic Sauerbery equation does not explicitly describe the effect of film stress, it will affect the QCM frequency and thus the effective shear modulus. The term "direct measurement" in QCM includes measurements of films deposited on or removed from the crystal surface of a piezoelectric crystal. The term "local measurement" in QCM refers to measurements of films deposited locally at or near a specific location.

[0017] Some known methods for process monitoring using integrated sensors include mass spectrometers, spectrometers, RF sensors, and vacuum gauges. However, such methods are not localized and cannot provide detailed information about the film accumulated or removed at different chamber locations. An example of nonlocal process control includes plasma cleaning methods such as emission spectroscopy, residual gas analyzers, and chamber impedance measurements. However, all of these methods measure complex signals from the entire chamber and do not identify the homogeneity or uniformity of the process material at different chamber locations. Other known sensors, such as temperature sensors, can be locally located and read measurements along the surfaces of various chamber components, but will not provide detailed information about the film conditions associated with coating these surfaces.

[0018] U.S. Patent Application Publication No. 2012 / 0201954 (Wajid) discloses a QCM that provides information about film coating or etching, but uses a single location and therefore cannot provide information about the uniformity or homogeneity of the process at different chamber locations. Furthermore, the accuracy and values ​​of the process data decrease as the chamber size increases.

[0019] U.S. Patent Application Publication No. 2014 / 0053779 (Martinson et al.) describes a QCM probe that moves between different chamber locations. However, this solution is limited to research laboratories and is only compatible with production environments where vacuum is required for manufacturing. Furthermore, this solution does not facilitate simultaneous monitoring of QCM sensors at different chamber locations.

[0020] Therefore, there is no direct local method for monitoring process uniformity / homogeneity inside chemical vapor deposition (CVD) and etching chambers. Summary of the Invention

[0021] Therefore, in one embodiment of this disclosure, a system for monitoring a semiconductor process is provided. The system includes a plurality of sensors and a microcontroller, wherein the plurality of sensors are disposed at various predetermined locations within a processing chamber. The microcontroller receives data from the plurality of sensors and measures the uniformity of the semiconductor process based on the data received from the plurality of sensors. At least one of the sensors defines an angular orientation different from that of another sensor. In yet another embodiment, at least two of the sensors each include a sensor surface defining an angular orientation, wherein the angular orientation of one sensor surface differs from that of the other sensor surface.

[0022] In another embodiment of this disclosure, a system for monitoring a semiconductor process is provided. The system includes a plurality of sensors selectively arranged within a processing chamber and configured to emit process signals indicative of a material process occurring in the vicinity of each of the plurality of sensors. A first sensor and a second sensor define a first spatial location and a second spatial location within the processing chamber, respectively, wherein the first spatial location has an angular orientation different from the second spatial location. A microcontroller, in response to the process signals, correlates the material process sensed by the first and second sensors with a semiconductor process currently occurring within the processing chamber. The sensors acquire information useful for performing a semiconductor process within either the current processing chamber or a related processing chamber.

[0023] In yet another embodiment, a method for monitoring a semiconductor process is provided. The method includes the steps of: placing a plurality of sensors at different locations within a processing chamber, and measuring the deposition or removal of process material on or from a substrate disposed within the processing chamber. A first sensor of the plurality of sensors has a first angular orientation and a first position within the processing chamber, and a second sensor of the plurality of sensors has a second angular orientation and a second position within the processing chamber. The method further includes the steps of: monitoring the deposition or removal of process material at the different locations within the processing chamber; and measuring the uniformity of the semiconductor process based on data received from the plurality of sensors during the monitoring step. The first angular orientation and first position of the first sensor, and the second angular orientation and second position of the second sensor, facilitate the measurement of the uniformity of the semiconductor process.

[0024] In yet another embodiment, a method for monitoring semiconductor processes in a processing chamber is provided, the method comprising the steps of: (i) providing a reference substrate having a thickness distribution that satisfies an acceptance profile; (ii) placing a plurality of sensors within the processing chamber to measure material process data occurring near each of the plurality of sensors; (iii) correlating the measured material process data with a thickness distribution of a test substrate produced within the processing chamber; (iv) comparing the thickness distribution of the test substrate with that of the reference substrate to determine whether the test substrate meets an acceptance criterion established by the acceptance profile of the reference substrate; (v) when the acceptance criterion of the test substrate has been met, recording the material process data measured by the sensors; and (vi) processing a subsequently manufactured substrate based on the recorded material process data.

[0025] The above embodiments are merely exemplary. Other embodiments as described herein are within the scope of the disclosed subject matter. Attached Figure Description

[0026] To understand the features of this disclosure, reference can be made to certain embodiments described in detail, some of which are illustrated in the accompanying drawings. However, it will be noted that the drawings illustrate only certain embodiments and are therefore not intended to limit its scope, as the scope of the disclosed subject matter also covers other embodiments. The drawings are not necessarily drawn to scale, and the emphasis is generally placed on the features illustrating certain embodiments. In the drawings, similar reference numerals are used throughout the various views to indicate similar parts, wherein:

[0027] Figure 1A and Figure 1B A chamber for semiconductor processing is described according to one or more aspects set forth herein;

[0028] Figure 2 Measurements from multiple sensors disposed within the processing chamber are depicted according to one or more aspects set forth herein; and

[0029] Figure 3A and Figure 3B A correlation diagram is depicted showing the measurements obtained from the wafer by two QCM sensors at different locations in the processing chamber of Figure 1.

[0030] Throughout the various views, corresponding reference characters indicate the corresponding parts. The examples presented herein illustrate several embodiments but should not be construed as limiting the scope in any way. Detailed Implementation

[0031] This disclosure relates to the field of semiconductor manufacturing, including semiconductor manufacturing control. More specifically, in one example, a sensor system monitors semiconductor manufacturing process tools, specifically directly monitoring chamber process homogeneity during deposition and etching processes. For example, a unique method is disclosed herein for direct thin film deposition and etching measurements using a sensor that allows localized monitoring of process homogeneity at one or more locations within a processing chamber. This provides insight into the local cross-effects between the substrate and the conditions of different chamber portions. Advantageously, due to the heterogeneity of the processes (deposition and / or etching) within the processing chamber, deploying sensors at different chamber locations will allow for the measurement of different film properties (mass density and stress).

[0032] Many different types of sensors can be employed in this disclosure. For example, quartz crystal microbalance (QCM) sensors or microelectromechanical (MEM) sensors can be deployed. One example of a MEMS sensor used in this disclosure is a surface acoustic wave sensor. Those skilled in the art will readily understand how QCM and MEMS sensors are made and used. This disclosure utilizes a variety of such sensors positioned at different locations within the processing chamber. In any embodiment where the QCM sensor is discussed, any other sensor, such as a MEMS sensor, can be employed.

[0033] In one or more embodiments, any combination of the following sensor types may be used as a sensor: capacitor sensor, photocathode, photodetector sensor, micromachined ultrasonic transducer, oscillator device configured to measure energy or mass changes, resonant electro-optical device, resistance measurement sensor, sensor having a dielectric waveguide in contact with a metal layer or metal pattern suitable for generating a plasma reaction, light-emitting device, electron beam source, ultrasonic source, optical resonator, microring resonator, photonic crystal structure resonator, and temperature sensor.

[0034] In one embodiment, the system uses the local sensitivity of a quartz crystal microbalance (QCM) sensor to perform deposition and etching processes (adding or removing mass and / or film stress on or from the QCM), the QCM sensor employing the resonant frequency of the crystal (under known process conditions). One embodiment of this disclosure also enables the use of a QCM sensor to monitor process conditions (e.g., temperature, flow rate, pressure, etc.) under known thickness and stress accumulation, to monitor local process conditions. Instead of a QCM sensor, a MEMS sensor can be used in the same manner.

[0035] By using QCM sensors at different locations within the processing chamber and sampling them during different process sequences (e.g., different deposition and etching steps), crucial information reflecting the real-time process homogeneity of the chamber can be obtained. MEM sensors can be used in the same manner instead of QCM sensors.

[0036] The above processes can be used in multiple chambers to implement chamber and tooling matching and can be used for process variation monitoring (between chambers and tools). Another benefit involves reducing the time to bring a specific processing chamber into production by using multiple sensors to measure the deposition rate. Wafer edge uniformity can also be obtained by using multiple sensors on the chamber radius surrounding the wafer edge. Process homogeneity monitoring of different sequences inside the HDP deposition chamber can include: pre-coating deposition homogeneity, wafer deposition homogeneity, plasma cleaning homogeneity monitoring, etc.

[0037] By measuring the QCM frequency values ​​at the start of the deposition sequence and ending with the plasma cleaning sequence (for a given production scheme), it is possible to obtain a measurement of process homogeneity. Furthermore, the frequency difference or delta between the start and end of different runs can provide information about the process stability at a given location.

[0038] Another example of process homogeneity measurement involves (for the same scheme) the frequency differences between different wafers between the start and end of wafer deposition. Specific relevant parameters or equations (based on QCM location) can then be calculated to predict wafer thickness and thickness variability. This can help avoid using test wafers for thickness measurements, or can be used as feedforward or feedback information to control different process operations before or after substrate deposition. Instead of QCM sensors, MEMS sensors can be used in the same manner.

[0039] By obtaining the maximum frequency from different QCM locations during plasma cleaning, process homogeneity can also be measured, allowing users to know whether the film is accumulating under-etching or over-etching at a particular location. Algorithms for determining the process endpoint can utilize frequency information from multiple QCM sensors dispersed at different locations and can be used to optimize the cleaning process endpoint (EP). For example, a moving average of the frequency derivative can be monitored until a threshold is reached—that is, when the cleaning endpoint is reached, the frequency derivative becomes significantly lower. This can be intentionally achieved or realized for different areas, for example, through over-etching or under-etching. The same or similar methods can be applied to other time-based processes using material addition or removal, such as primers, pre-coats, etc.

[0040] Endpoint detection for wafer-based processes using plasma or heat (pretreatment or baking), such as deposition, etching, densification, and contaminant removal, can also be achieved using signal inputs from multiple QCM sensors distributed at different locations. QCM sensors at different locations within the processing chamber can measure different deposition and etching rates to provide information about process uniformity; for example, in the case of a plug nozzle (in the HDP chamber), the QCM closure associated with that specific nozzle can measure different process rates. Instead of QCM sensors, MEMS sensors can be used in the same manner.

[0041] Furthermore, by implementing at least two QCM sensors (each with a different angular orientation (relative to the wafer plane)), process rates at different angles above the wafer can be measured and / or calculated to provide three-dimensional information about the process and process rate in the wafer plane. Moreover, by applying a variable DC bias to the QCM relative to the chamber ground, the deposition sputtering ratio on the wafer can be generated above the QCM using a tool factor.

[0042] Controlling process uniformity is crucial for integrating different processes and has a direct impact on chip yield. Figure 1AA chamber 100 for semiconductor processing, such as a PECVD chamber, is depicted. Multiple QCM sensors 102 are deployed at or near various locations within or near the chamber 100, such as the sidewalls, bottom, or top. A heated stage 104 supporting a substrate 108 (i.e., the wafer) is deployed within the chamber, and one or more QCM sensors 102 may be deployed within the heated stage 104. The chamber 100 includes a top electrode 106 and a gas inlet 110, both of which can also be monitored by the additional QCM sensors 102. The chamber further includes pumps 112 to remove process gases, and these pumps 112 can also be monitored by the additional QCM sensors 102. Although this particular example uses QCM sensors, MEMS sensors can be used in the same manner.

[0043] Figure 1B Another chamber 101 for semiconductor processing is depicted. Figure 1B In this embodiment, multiple QCM sensors 102 are deployed at different locations within the processing chamber and at different locations and / or angles relative to the substrate. Advantageously, the multiple QCM sensors 102 allow for process (and process rate) homogeneity monitoring at different locations throughout the processing chamber and at different chamber portions. Based on the data collected from the multiple QCM sensors 102, the process can be optimized, and process homogeneity issues can be detected within the processing chamber. For example, due to the use of multiple QCM sensors 102 at different angles and locations, the system allows for process and process rate monitoring and control on the substrate. Additionally, the data can be used to generate a 3D map of process homogeneity. Although this particular example uses QCM sensors, MEM sensors can be used in the same manner.

[0044] like Figure 2 As depicted, the deposition (or the difference in fundamental frequency across the wafer) measured by two differently positioned QCM sensors (inside the deposition chamber) differs for wafer-to-wafer variations. While sensor 2 (FF2) data is stable for wafer-to-wafer, sensor 1 reflects a higher degree of process variation for wafer-to-wafer.

[0045] For example, the endpoint of chamber wall cleaning can be determined by a single QCM sensor within the chamber. Different cleaning schemes are anticipated to clean the chamber wall to its endpoint. Disadvantageously, the use of a single QCM does not adequately determine the cleanliness of the entire chamber wall. It will be understood that the effective area of ​​a QCM is very small compared to the large, dispersed area of ​​the chamber wall. Therefore, it is possible to use several QCM sensors at selected locations to fairly represent the average chamber cleaning conditions. This not only helps to conclude that the chamber wall is sufficiently clean, but the slope of the etching frequency can also be used to understand the non-uniformity of the etching rate, which can then be used to bring the chamber to acceptable conditions.

[0046] Another advantage of using multiple sensors involves determining the right trade-off between under-etching and over-etching conditions. For example, heterogeneous etching inside a chamber can over-clean one part of the chamber, leaving another part incompletely cleaned, resulting in constant over-etching of the chamber walls (transforming the chamber material into particles) and / or incremental film buildup (ultimately leading to stress-relieving spalling of the particles). Therefore, by having several QCM sensors, information about this heterogeneity can be used in real time. Furthermore, the process can be modified to produce a satisfactory trade-off until a stable solution is found. This also helps to tune a given chamber to the conditions for a golden chamber.

[0047] One embodiment of this disclosure employs a system including multiple QCM sensors to monitor, learn, and control real-time chamber process homogeneity. The system also provides insights into the cross-effects between substrate processing (etching and deposition) at different or the same time stamps to different chamber portion locations. Process homogeneity can be measured (by comparing QCM signals at different locations) by monitoring different chamber locations at the same time stamp. By monitoring QCM with different process sequences (e.g., undercoating, deposition, etching, cleaning, etc.), process homogeneity can be monitored for each process sequence or for subsequent processing in different processing chambers.

[0048] One embodiment of this disclosure allows for homogeneity monitoring of the deposition and etching of stacks of layers (more than one layer) that may consist of different materials, different layer stresses, and different densities. One example includes multiple layers deposited on an oxide-over-nitride (ON) stack, as used in 3D NAND technology. Currently, there are no direct measurements to allow for on-site process control. The use of QCM sensors at multiple locations within the stack deposition chamber allows for real-time monitoring. QCM data can be fed back into the process as process parameters, for example, allowing for real-time modification of the deposition time of individual layers in the stack based on QCM sensor data.

[0049] The system disclosed herein can be used in multiple chambers to compare and achieve better chamber matching, thereby reducing overall process variability. The multiple sensors provide the benefit of verifying the reproducibility of homogeneity between wafers and the spatial homogeneity of a given wafer. This data also allows for homogeneity analysis throughout the process steps (for root cause analysis purposes) by incorporating frequency shifts into a statistical bin to understand which specific steps in the process cause the variation.

[0050] In one example, the system includes multiple single or dual QCM sensors mounted at carefully selected locations on the processing chamber to monitor changes in the deposition and / or etching of thin films on the chamber walls closely adjacent to the respective sensors. Such conditions may, among others, include the mass, stress, density, and / or material composition above the QCM sensors. QCM data can also be used to correlate other process conditions (flow rate, pressure, temperature, plasma homogeneity, etc.), as those variations will affect the measured QCM fundamental frequency (at different harmonics) and possibly the rate of change of that fundamental frequency. Although this particular example uses QCM sensors, MEMS sensors can be used in the same manner.

[0051] In this scenario, the software communicates with the process tools and QCM sensors. The SW will likely receive logical information from the process tools, such as production sequence name, batch ID, bay ID, wafer ID (WID), sequence ID, etc., to supplement other variables, such as step number, RF power, pressure, etc. The combination of tool data and QCM sensors will allow for the measurement of local process homogeneity between different process steps and within different process steps.

[0052] In another example, the software can communicate with multiple chambers during the same process operation, enabling comparison and matching between different chambers. The software can communicate with multiple chambers at different times during the process. That is, information collected from one operation can be fed forward or backward to different operations to improve process stability and reduce variability.

[0053] By measuring the predicted wafer thickness using QCM data, the data can flow forward to subsequent operations and be used to calculate the endpoint time in CMP operations. The methods and systems disclosed herein enable the generation of real-time 3D thickness wafer maps or any other metrology measurements for the processing chamber. The QCM system will first “learn” the correlation factors between QCM sensor data and wafer thickness (or any other metrology measurement) at each X / Y location on the wafer. This can be accomplished by “training” the QCM sensor using thickness metrology maps from external stand-alone metrology equipment (or other in-situ metrology) by using the correlation between QCM sensor data at different locations (and / or different angles) within the processing chamber and different X / Y locations (coordinates) on the wafer. Based on the correlation factors, wafer thickness, stress maps, or other measurements of interest can be derived in real-time from the QCM sensor system data.

[0054] The method may further include providing multiple QCM sensors and a process monitor inside the processing chamber, which monitors the material thickness throughout the deposition chamber while recording QCM data. Wafer thickness can be measured at multiple locations on the substrate or wafer using external metrology tools. Statistical models can be generated using QCM data or mathematical transformations that take QCM data as input and output substrate thickness distribution data (or mathematical transformations to that data) at different locations on the substrate or wafer. Based on the calculated data, a 3D (X / Y position, Z = thickness) surface distribution can be generated in real time using the QCM data. Finally, substrate thickness distribution or other metrological measurements can be automatically imported into the QCM sensor system, and the QCM data can be automatically optimized.

[0055] Figure 3A and Figure 3B A correlation diagram is depicted between two QCM sensors at different locations throughout the processing chamber in Figure 1. For example, Figure 3A and Figure 3B Thickness maps at 49 locations measured by external metrology tools are shown to determine which areas strongly correlate with the results from the QCM sensors. FF1 represents sensor 1, and FF2 represents sensor 2. Some areas indicate strong correlations, while others indicate weaker correlations.

[0056] Figure 3A The relevant diagram is shown, in which the QCM sensor S1 is positioned approximately at the 11 o'clock position relative to the substrate or wafer. Figure 3B A correlation diagram is shown, in which the QCM sensor S2 is positioned approximately at the 6 o'clock position relative to the substrate or wafer. Therefore, a better understanding of the thickness distribution of the substrate or wafer can be determined based on the combined data from the at least two sensors S1 and S2. For example, a weighted average of the sensor data can be used, where the closer a sensor is to the region of interest, the more weight can be assigned to that sensor. Although this particular example uses a QCM sensor, a MEM sensor can be used in the same manner.

[0057] It is possible to obtain a high correlation factor between sensors adjacent to each other at X / Y locations on the wafer (between thickness data and sensor data at X / Y locations). For example, sensor S1 shows a high correlation with thickness data when located near the sensor, and the same applies to sensor S2. The multi-QCM system used can be either the same crystal cut or a combination of different cuts, depending on the location of the given QCM sensor being selected to minimize the effects of temperature or pressure.

[0058] The choice of QCM type is also based on the process steps, as there can be more than one cut at the same location for different process steps. QCM sensors can be rigidly mounted to the chamber or are allowed to extend, retract, tilt, and / or rotate (using a motor) dynamically to capture different process conditions (mass removal, mass deposition, densification, temperature, pressure, membrane stress) at different angles and locations within the processing chamber.

[0059] Using in-situ dynamic adjustment of the QCM sensor position and angle during different process sequences will allow for the measurement and possible control of different process / process rate conditions or endpoints at different parts of the chamber, as well as the monitoring and / or control of different process sequences for different angles and positions in wafer planning.

[0060] One or more embodiments described above may also be modified as follows. For example, in some embodiments, the QCM (or MEM) sensor may use one or more of the following power supply devices: a wired power supply, wireless charging, a battery, a capacitor or so-called supercapacitor or any other thermal power source. Additionally, in other embodiments, signals are transmitted from the QCM sensor, and these signals may be transmitted via wired or any other wireless method (e.g., Bluetooth or RF).

[0061] The associated method between a circular substrate and a cylindrical processing chamber can be performed in the following manner. In this example, the cylindrical processing chamber can be divided into eight (8) or more sectors, each sector having a substantially pie-shaped geometry, i.e., two radii emanating from the center of the chamber, the outer arc of the cylindrical inner wall of the processing chamber, and a vertical height. For each sector, a QCM or MEM sensor is placed at a predefined location within that sector to obtain a sensed value of the accumulated mass on the sensor. The signal provided by the QCM sensor is a frequency variation or differential frequency value, which indicates the accumulated mass value. If the circular substrate is centrally located within the processing chamber, the eight sectors will define eight pie-shaped regions.

[0062] Any process parameter of interest (such as thickness values) can be defined for each region of the substrate and represented as thickness values ​​T1-T8. Then, it can be expressed in the form T i =F(f S1 , .. f S8 The mathematical equations of f mathematically correlate the sensor readings S1-S8 with the thickness values ​​T1-T8, where f si It is a function of sensor values ​​S1-S8, and F is all f siThe function F can then be represented by an 8x8 matrix that correlates each of the sensor values ​​with the thickness value of the substrate. This correlation takes the form T = C(f), where T is a vector of eight thickness values ​​T1-T8, C is a constant 8x8 matrix, and f is a vector of sensor values ​​S1-S8.

[0063] The applicant discovered that the C-matrix is ​​time-invariant for a given set of process parameters (such as pressure, flow rate, plasma power, etc.). Therefore, one or more calibration runs can be performed to determine the C-matrix at a specific time. The values ​​of the C-matrix can then be used to measure process parameters for subsequent runs of the process. For example, a calibration or reference wafer substrate can be exposed to a given process scheme in a chamber with eight sensors positioned as described above. The calibration wafer can then be measured using any of various techniques (such as ellipsometry). Based on these measurements, the C-matrix can be determined. By using the aforementioned matrix equation to determine process parameters (such as thickness), subsequent runs of the process on the production wafer substrate can be periodically checked for compliance with design tolerances.

[0064] Additional embodiments include any of the embodiments described above, wherein one or more of its components, functions, or structures are interchanged, replaced, or enhanced by one or more of the components, functions, or structures of the different embodiments described above.

[0065] It should be understood that various changes and modifications to the embodiments described herein will be apparent to those skilled in the art. Such changes and modifications can be made without departing from the spirit and scope of this disclosure and without diminishing its intended advantages. Therefore, such changes and modifications are intended to be covered by the appended claims.

[0066] Although several embodiments of the present disclosure have been disclosed in the foregoing description, those skilled in the art will understand that many modifications and other embodiments of the present disclosure will conceive of in connection with the present disclosure, taking advantage of the teachings presented in the foregoing description and the associated drawings. Therefore, it should be understood that the present disclosure is not limited to the specific embodiments disclosed above, and many modifications and other embodiments are intended to be included within the scope of the appended claims. Furthermore, although specific terminology is used herein and in the following appended claims, it is used in a general and descriptive sense only and not for the purpose of limiting the present disclosure or the following appended claims.

Claims

1. A system for monitoring a semiconductor process, the system comprising: Multiple sensors, selectively arranged within a processing chamber and configured to emit process signals indicative of material processes occurring in the vicinity of each of the multiple sensors, the multiple sensors comprising: A first sensor defines a first spatial position within the processing chamber, and A second sensor defines a second spatial position within the processing chamber, wherein the first spatial position includes an angular orientation different from the second spatial position; and Microcontrollers are constructed as Receive the process signal, The material processes sensed by the first and second sensors are correlated with the semiconductor processes occurring within the current processing chamber to obtain information useful for performing semiconductor processes. The parameters of the semiconductor process are modified in real time based on the process signals.

2. The system according to claim 1, wherein, The semiconductor process involves a deposition process on a substrate or a removal process from a substrate.

3. The system according to claim 1, wherein, The semiconductor process involves the material thickness distribution of the substrate.

4. The system according to claim 3, wherein, The microcontroller determines the uniformity of the material thickness distribution.

5. The system according to claim 1, wherein, Process signals emitted by at least two sensors are correlated to obtain information about semiconductor processes occurring in the region between the at least two sensors.

6. The system according to claim 1, wherein, The plurality of sensors are arranged around the edge of the substrate, and the microcontroller controls semiconductor processes to maintain substrate edge uniformity.

7. The system according to claim 1, wherein, The substrate within the processing chamber defines a two-dimensional plane, wherein the first sensor and the second sensor each have a different angular orientation relative to the two-dimensional plane, and wherein the microcontroller generates three-dimensional thickness distribution data relating to the substrate.

8. The system according to claim 1, wherein, The plurality of sensors include a quartz crystal microbalance (QCM) sensor.

9. The system according to claim 1, wherein, The plurality of sensors include microelectromechanical (MEM) sensors.

10. The system according to claim 1, wherein, The plurality of sensors include one or more of a quartz crystal microbalance (QCM) sensor and a microelectromechanical (MEM) sensor.

11. A system for monitoring a semiconductor process, the system comprising: Multiple sensors disposed within a processing chamber are configured to monitor deposition or removal processes at different locations within the processing chamber. A first sensor among the multiple sensors has a first angular orientation and a first position within the processing chamber, and a second sensor among the multiple sensors has a second angular orientation and a second position within the processing chamber. A microcontroller for receiving data from the plurality of sensors, wherein the microcontroller measures the uniformity of the semiconductor process based on the data received from the plurality of sensors and modifies parameters of the semiconductor process in real time based on the data received from the first sensor and the second sensor, wherein the first angular orientation and the first position of the first sensor and the second angular orientation and the second position of the second sensor facilitate the measurement of the uniformity of the semiconductor process.

12. The system according to claim 11, wherein, The plurality of sensors include a quartz crystal microbalance (QCM) sensor.

13. The system according to claim 11, wherein, The plurality of sensors include microelectromechanical (MEM) sensors.

14. The system according to claim 11, wherein, The plurality of sensors includes one or more of quartz crystal microbalance (QCM) sensors and microelectromechanical (MEMS) sensors, and wherein, The substrate within the processing chamber defines a two-dimensional plane; The first sensor and the second sensor have different angular orientations relative to the two-dimensional plane; The microcontroller generates three-dimensional thickness distribution data related to the substrate.

15. The system according to claim 11, wherein, The semiconductor process is a material deposition process, and the microcontroller measures the uniformity of the material deposition in the chamber.

16. The system according to claim 11, wherein, The semiconductor process includes a material deposition process, and the microcontroller measures the material thickness in the chamber.

17. The system according to claim 11, wherein, The semiconductor process includes a material removal process, and the microcontroller measures the uniformity of material removal in the chamber.

18. The system according to claim 11, wherein, The semiconductor process includes a material removal process, and the microcontroller measures the material thickness in the chamber.

19. A method for monitoring a semiconductor process, the method comprising: Multiple sensors are provided within the processing chamber, the multiple sensors including: The first sensor of the plurality of sensors has a first angular orientation and a first position in the processing chamber, and The second sensor among the plurality of sensors has a second angular orientation and a second position in the processing chamber; Monitor the deposition or removal process at different locations within the processing chamber; The uniformity of the semiconductor process is measured based on data received from the plurality of sensors during the monitoring step, wherein the first angular orientation and first position of the first sensor and the second angular orientation and second position of the second sensor facilitate the measurement of the uniformity of the semiconductor process; and The parameters of the semiconductor process are modified in real time based on the data received from the first sensor and the second sensor.

20. The method according to claim 19, wherein, The multiple sensors include multiple quartz crystal microbalance (QCM) sensors.

21. The method according to claim 19, wherein, The plurality of sensors include microelectromechanical (MEM) sensors.

22. The method according to claim 19, wherein, The plurality of sensors includes one or more of quartz crystal microbalance (QCM) sensors and microelectromechanical (MEMS) sensors, and wherein, The substrate within the processing chamber defines a two-dimensional plane; The first sensor and the second sensor have different angular orientations relative to the two-dimensional plane; The microcontroller generates three-dimensional thickness distribution data related to the substrate.

23. The method according to claim 19, wherein, The semiconductor process is a material deposition process, and the microcontroller measures the uniformity of the material deposition in the chamber.

24. The method according to claim 19, wherein, The semiconductor process is a material deposition process, and the microcontroller measures the material thickness in the chamber.

25. The method according to claim 19, wherein, The semiconductor process is a material removal process, and the microcontroller measures the uniformity of material removal in the chamber.

26. The method according to claim 19, wherein, The semiconductor process is a material removal process, and the microcontroller measures the material thickness in the chamber.

27. A method for monitoring a semiconductor process in a processing chamber, the method comprising the steps of: Provide a reference substrate with a thickness distribution that meets acceptance requirements; Multiple sensors are placed within the processing chamber to measure material process data occurring in the vicinity of each of the multiple sensors, wherein the multiple sensors include: A first sensor defines a first spatial position within the processing chamber, and A second sensor defines a second spatial position within the processing chamber, wherein the first spatial position defines an angular orientation different from the second spatial position; The measured material process data are correlated with the thickness distribution of the test substrate produced in the processing chamber; The thickness distribution of the test substrate is compared with that of the reference substrate to determine whether the test substrate meets the acceptance criteria established by the acceptance distribution of the reference substrate, and when the acceptance criteria of the test substrate are met, the material process data measured by the sensor are recorded; and The substrates subsequently manufactured are processed based on the recorded material process data.

28. The method according to claim 27, wherein, The substrate within the processing chamber defines a two-dimensional plane, wherein at least one of the first and second sensors is misaligned relative to the two-dimensional plane, and wherein the microcontroller generates three-dimensional thickness distribution data relating to the substrate.

29. The method of claim 27, further comprising the following steps: Dynamically mount at least one of the plurality of sensors to reposition the at least one sensor from an initial position to a final position; measure the material process data of the at least one sensor at the initial position and the final position; And correlate the measured material process data with the thickness distribution of the test substrate.

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