Method and mechanism for adjusting process chamber parameters during substrate manufacturing - Patents.com

Abstract

1. An electronic device manufacturing system capable of acquiring metrology data generated using a metrology device located in a process chamber that performs a deposition process on a substrate according to a process recipe, the process recipe including a plurality of set parameters, the deposition process producing a plurality of film layers on a surface of the substrate. The manufacturing system may further generate a correction profile based on the metrology data. The manufacturing system may further generate an updated process recipe by applying the correction profile to the process recipe. The manufacturing system may further cause an etching process to be performed on the substrate according to the updated process recipe.

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE This disclosure relates to electrical components and, more particularly, to methods and mechanisms for adjusting process chamber parameters during substrate manufacturing. [Background technology]

[0002] A product may be produced by performing one or more manufacturing processes using a manufacturing apparatus. For example, a semiconductor manufacturing apparatus may be used to produce a semiconductor device (e.g., a substrate) through a semiconductor manufacturing process. The manufacturing apparatus may deposit multiple layers of films on a surface of the substrate according to a process recipe and may perform an etching process to form an intricate pattern in the deposited films. For example, the manufacturing apparatus may perform a chemical vapor deposition (CVD) process to deposit a replacement layer on the substrate. Sensors may be used to determine manufacturing parameters of the manufacturing apparatus during the manufacturing process, and metrology devices may be used to determine characteristic data of a product produced by the manufacturing apparatus, such as an overall thickness of a layer on the substrate. Generally, the manufacturing apparatus may monitor individual sensors to detect problems during the deposition process. However, monitoring individual sensors does not provide correction data during the manufacturing process, which may result in an inferior or unusable product. Therefore, a system that can generate correction data during the manufacturing process is desirable. Summary of the Invention

[0003] The following is a simplified summary of the disclosure to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure, nor to delineate the scope or claims of particular embodiments of the disclosure. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0004] In one aspect of the disclosure, an electronic device manufacturing system includes a transfer chamber, a process chamber coupled to the transfer chamber, and a metrology device located within at least one of the transfer chamber or the process chamber. The manufacturing system is configured to acquire metrology data generated using the metrology device located within the process chamber performing a deposition process on a substrate according to a process recipe, the process recipe including a plurality of set parameters, the deposition process producing a plurality of film layers on a surface of the substrate. The manufacturing system can further generate a correction profile based on the metrology data. The manufacturing system can further generate an updated process recipe by applying the correction profile to the process recipe. The manufacturing system can further cause an etch process to be performed on the substrate according to the updated process recipe.

[0005] Further aspects of the present disclosure include methods according to any aspect or embodiment described herein.

[0006] A further aspect of the present disclosure includes a non-transitory computer-readable storage medium comprising instructions that, when executed by a processing device operably coupled to a memory, perform operations according to any aspect or embodiment described herein.

[0007] The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram illustrating an exemplary system architecture, in accordance with certain embodiments. [Diagram 2] 1 is a flow diagram of a method for training a machine learning model, according to certain embodiments. [Diagram 3] FIG. 1 is a top schematic view of an exemplary manufacturing system in accordance with certain embodiments. [Figure 4] 1 is a cross-sectional schematic side view of an exemplary process chamber of an exemplary manufacturing system, in accordance with certain embodiments. [Diagram 5] 4 is a flow diagram of a method for determining a correction profile for a process recipe, in accordance with certain embodiments. [Figure 6] 1 is a flow diagram of a method for adjusting a process recipe, in accordance with certain embodiments. [Figure 7] 1 is a flow diagram of a method for determining a correction profile using a machine learning model, according to certain embodiments. [Figure 8] FIG. 1 is a block diagram illustrating a computer system in accordance with certain embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Described herein is technology directed to methods and mechanisms for adjusting process chamber parameters during substrate fabrication. A film can be deposited on a surface of a substrate during a deposition process (e.g., deposition (CVD) process, atomic layer deposition (ALD) process, etc.) performed in a process chamber of a fabrication system. For example, in a CVD process, the substrate is exposed to one or more precursors that react on the substrate surface to produce a desired deposit. The film can include one or more layers of material formed during the deposition process, with each layer including a particular thickness gradient (e.g., thickness variation along a layer of the deposited film). For example, a first layer can be formed directly on the surface of the substrate (referred to as the proximal layer or proximal end of the film) and have a first thickness. After the first layer is formed on the surface of the substrate, a second layer having a second thickness can be formed on the first layer. This process continues until the deposition process is completed and a final layer of the film (referred to as the distal layer or distal end of the film) is formed. The film can include alternating layers of different materials. For example, the film may include alternating layers of oxide and nitride layers (oxide-nitride-oxide-nitride stack or ONON stack), alternating layers of oxide and polysilicon layers (oxide-polysilicon-oxide-polysilicon stack or OPOP stack), etc.

[0010] The film may be subjected to, for example, an etching process to form a pattern on the surface of the substrate, a chemical mechanical polishing (CMP) process to smooth the surface of the film, or any other process necessary to produce a finished substrate. The etching process may involve exposing a high energy process gas (e.g., plasma) to the sample surface to decompose the material at the surface, which may then be removed by a vacuum system.

[0011] The process chambers can perform each substrate manufacturing process (e.g., a deposition process, an etching process, a polishing process, etc.) according to a process recipe. The process recipe defines a particular set of operations to be performed on the substrate during the process and can include one or more settings associated with each operation. For example, a deposition process recipe can include a process chamber temperature setting, a process chamber pressure setting, flow rate settings for precursors of materials included in the film deposited on the substrate surface, etc. Thus, the thickness of each film layer can be correlated to these process chamber settings.

[0012] During the substrate manufacturing process, the process chamber may experience degradation conditions, such as accumulation of contaminants, corrosion of certain components, etc. These degradation conditions may result in defects in the substrate (e.g., deviation of actual layer thickness from expected layer thickness, non-uniform layer thickness, incomplete etching, etc.). For example, non-uniform layer thickness may move the gas distribution plate closer to or farther from the surface of the substrate, thus affecting plasma flow and / or temperature during the subsequent etching process. This may lead to poor product quality, reduced manufacturing yield, and significant downtime and increased repair time. In another example, during the start of a production run (e.g., processing a batch of substrates), the first substrate may be processed and used to calibrate the process chamber. In particular, once the first substrate is processed, metrology data may be extracted from the substrate, and based on this metrology data, a technician may manually adjust the settings for the remainder of the batch. However, this is a time-consuming and error-prone process that may result in delays in achieving optimal process chamber settings (e.g., process gas pressures and flow rates, temperature, etc.), thus causing manufacturing delays and / or film distortion.

[0013] Aspects and implementations of the present disclosure address these and other shortcomings of existing techniques by generating a correction profile based on data generated by a metrology device located within the process chamber. In particular, the metrology device can be included within or coupled to the process chamber (referred to as an "in-situ metrology device"). The in-situ metrology device can be configured to generate metrology data for the substrate before, during, and / or after a manufacturing process (e.g., deposition process, etch process, etc.) while the substrate remains within the process chamber. In some embodiments, the in-situ metrology device can include an optical instrument (e.g., an IR camera) for measuring dimensions (e.g., thickness, height, etc.) of a film or etch on the substrate. For example, the in-situ metrology device can use the optical instrument to obtain dimensional data at approximately 3,000 locations along the entire substrate. In another example, the in-situ metrology device can use the optical instrument to obtain dimensional data only along the edge of the substrate, which can be used to improve the etch process along the edge of the deposited film. The in-situ metrology device can use the metrology data to generate a thickness profile indicative of one or more thickness values ​​across the surface of the substrate. The thickness profile can indicate the thickness of a film on a substrate, the depth of each etch on a substrate, and the like.

[0014] The in-situ metrology tool can generate thickness profiles at various points during a manufacturing process. For example, a first step of a process recipe can be performed on a substrate, and then the in-situ metrology tool can generate a first thickness profile of the substrate. Then, a second step of the process recipe can be performed on the substrate, and then the in-situ metrology tool can generate a second thickness profile of the substrate, and so on.

[0015] The thickness profile can be used by the manufacturing system to generate a correction profile. The correction profile can include one or more adjustments or offsets (e.g., corrective actions) applied to parameters of the process recipe or the process chamber. For example, the correction profile can include adjustments to the temperature setting of the process chamber, the pressure setting of the process chamber, the flow rate setting of a precursor of a material included in the film deposited on the substrate surface, the power supplied to the process chamber, the ratio of two or more settings, etc. The manufacturing system can generate the correction profile by determining the difference between an expected profile (e.g., a thickness profile expected to be generated by the process recipe) and the generated thickness profile. For example, the manufacturing system can generate the correction profile by applying an algorithm to the difference and / or by searching for the difference in a library of known faults, where each difference is associated with a correction profile. The correction profile can be applied to one or more steps of a deposition process, an etch process, etc. In some embodiments, each correction profile can be applied to a subsequent step of the process recipe upon completion of the current process step. This allows the manufacturing equipment to compensate for deviations that have already occurred during the manufacturing of the current substrate. In other embodiments, each correction profile can be applied to the process step associated with the metrology data used to generate the correction profile for subsequent substrates. This allows the manufacturing system to generate adjustments for manufacturing subsequent substrates.

[0016] In some embodiments, the manufacturing system can perform a chamber matching operation on the second process chamber based on the correction profile. Chamber matching can be used to minimize process variations between substrates due to hardware variations. For example, the hardware variations can include different chuck temperatures, different lid temperatures, differences in power supply to each process chamber, differences in gas flow and / or pressure, etc. The chamber matching operation can include adjusting one or more parameters of the second process chamber or adjusting one or more parameters of a recipe used to generate the substrate in the second process chamber.

[0017] In some embodiments, the predictive system can train a machine learning model and apply it to current sensor values ​​associated with the thickness profile to generate an output, such as one or more values ​​indicative of a correction to one or more parameters of a process recipe. In some embodiments, the output is a value indicative of a difference between an expected thickness profile of a film on the substrate and an actual thickness profile of a film on the substrate. In some embodiments, the system can then compare the difference (between the expected thickness profile and the actual thickness profile) to a library of known fault (e.g., deviation) patterns to determine a correction profile. In some embodiments, the system performs a corrective action based on the correction pattern to adjust one or more parameters of a deposition process recipe (e.g., a temperature setting of a process chamber, a pressure setting of a process chamber, a flow rate setting of a precursor of a material included in the film deposited on the substrate surface, etc.), one or more parameters of an etching process, or one or more parameters of any other manufacturing process.

[0018] Aspects of the present disclosure provide the technical advantage of a significant reduction in the time required to perform parameter optimization of a process recipe. The disclosed configuration allows the system to periodically characterize the process chamber, such as during each step of the process recipe. Aspects of the present disclosure further provide technical advantages such as a significant reduction in the time to detect issues or failures experienced by a substrate during the manufacturing process, as well as improved energy consumption. The present disclosure also allows diagnostic data to be generated and corrective actions to be taken to avoid inconsistent, abnormal products, and unscheduled user time or downtime.

[0019] FIG. 1 illustrates an exemplary computer system architecture 100 according to aspects of the disclosure. In some embodiments, the computer system architecture 100 may be included as part of a manufacturing system for processing substrates, such as the manufacturing system 300 of FIG. 3. The computer system architecture 100 includes a client device 120, a manufacturing tool 124, a metrology tool 128, a prediction server 112 (e.g., for generating prediction data, providing model adaptation, using a knowledge base, etc.), and a data store 140. The prediction server 112 may be part of a prediction system 110. The prediction system 110 may further include server machines 170 and 180. The manufacturing tool 124 may include a sensor 126 configured to capture data of a substrate being processed in the manufacturing system. In some embodiments, the manufacturing tool 124 and the sensor 126 may be part of a sensor system including a sensor server (e.g., a field service server (FSS) at a manufacturing facility) and a sensor identifier reader (e.g., a radio frequency identification (RFID) reader of a front opening unified pod (FOUP) for the sensor system). In some embodiments, the metrology device 128 may be part of a metrology system that includes a metrology server (eg, metrology database, metrology folder, etc.) and a metrology identifier reader (eg, a FOUP RFID reader for the metrology system).

[0020] The manufacturing tool 124 may operate according to a recipe or over a period of time to produce products such as electronic devices. The manufacturing tool 124 may include a process chamber, such as the process chamber 400 described with respect to FIG. 4. The manufacturing tool 124 may perform a process on a substrate (e.g., a wafer, etc.) in the process chamber. Examples of substrate processes include a deposition process to deposit one or more layers on a surface of the substrate, an etch process to form a pattern on the surface of the substrate, etc. The manufacturing tool 124 may perform each process according to a process recipe. A process recipe defines a particular sequence of operations to be performed on the substrate during the process and may include one or more settings associated with each operation. For example, a deposition process recipe may include a temperature setting for the process chamber, a pressure setting for the process chamber, flow settings for precursors of materials included in the film to be deposited on the substrate surface, etc.

[0021] In some embodiments, the manufacturing tools 124 include sensors 126 configured to generate data associated with a substrate processed in the manufacturing system 100. For example, a process chamber may include one or more sensors configured to generate spectral or non-spectral data associated with a substrate before, during, and / or after a process (e.g., a deposition process) is performed on the substrate. In some embodiments, the spectral data generated by the sensors 126 may indicate a concentration of one or more materials deposited on a surface of the substrate. The sensors 126 configured to generate spectral data associated with the substrate may include reflectance sensors, ellipsometric sensors, thermal spectral sensors, capacitive sensors, etc. The sensors 126 configured to generate non-spectral data associated with the substrate may include temperature sensors, pressure sensors, flow sensors, voltage sensors, etc. Further details regarding the manufacturing tools 124 are provided with respect to FIGS. 3 and 4.

[0022] In some embodiments, the sensors 126 provide sensor data (e.g., sensor values, characteristics, trace data) associated with the manufacturing equipment 124 (e.g., associated with the manufacturing equipment 124 producing a corresponding product, such as a wafer). The manufacturing equipment 124 can produce a product by performing an operation according to a recipe or over a period of time. The sensor data received over a period of time (e.g., corresponding to at least a portion of a recipe or operation) can be referred to as trace data received from different sensors 126 over time (e.g., historical trace data, current trace data, etc.). The sensor data can include one or more values ​​of temperature (e.g., heater temperature), spacing (SP), pressure, high frequency radio frequency (HFRF), electrostatic chuck (ESC) voltage, current, material flow, power, voltage, etc. The sensor data can be associated with or indicative of hardware parameters, such as settings or components (e.g., size, type, etc.) of the manufacturing equipment 124, or manufacturing parameters, such as process parameters of the manufacturing equipment 124. The sensor data can be provided while the manufacturing equipment 124 is performing a manufacturing process (e.g., equipment readings as the product is processed). The sensor data may vary from board to board.

[0023] The metrology tools 127, 128 can provide metrology data associated with substrates processed by the fabrication tool 124. The metrology data can include values ​​of film property data (e.g., wafer spatial film properties), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. In some embodiments, the metrology data can further include values ​​of one or more surface profile property data (e.g., etch rate, etch rate uniformity, critical dimension of one or more features included in the surface of the substrate, critical dimension uniformity across the surface of the substrate, edge placement error, etc.). The metrology data can be of finished or semi-finished products. The metrology data can vary from substrate to substrate. The metrology data can be generated using, for example, reflectometry techniques, ellipsometry techniques, TEM techniques, etc.

[0024] The metrology tool 127 may be included as part of the fabrication tool 124. For example, the metrology tool 127 may be included within or coupled to a process chamber and configured to generate metrology data of the substrate before, during, and / or after a process (e.g., a deposition process, an etch process, etc.) while the substrate remains in the process chamber. In some examples, the metrology tool 127 may be referred to as an in-situ metrology tool. In another example, the metrology tool 127 may be coupled to another station of the fabrication tool 124. For example, the metrology tool may be coupled to a transfer chamber, such as the transfer chamber 310 of FIG. 3, a load lock, such as the load lock 320, or a factory interface, such as the factory interface 306.

[0025] The client device 120 may include computing devices such as a personal computer (PC), a laptop, a mobile phone, a smart phone, a tablet computer, a netbook computer, a network-connected television ("smart TV"), a network-connected media player (e.g., a Blu-ray player), a set-top box, an over-the-top (OTT) streaming device, an operator box, etc. In some embodiments, metrology data may be received from the client device 120. The client device 120 may display a graphical user interface (GUI) through which a user may provide as input metrology measurements of substrates processed in the manufacturing system. The client device 120 may include a corrective action component 122. The corrective action component 122 may receive user input of instructions (e.g., via a graphical user interface (GUI) displayed via the client device 120) associated with the manufacturing equipment 124. In some embodiments, the corrective action component 122 sends instructions to the prediction system 110, receives output (e.g., prediction data) from the prediction system 110, determines a corrective action based on the output, and causes the corrective action to be implemented. In some embodiments, the corrective action component 122 receives corrective action instructions from the predictive system 110 and causes the corrective action to be implemented. Each client device 120 may include an operating system that enables a user to one or more of create, view, or edit data (e.g., instructions associated with a manufacturing equipment 124, corrective actions associated with a manufacturing equipment 124, etc.).

[0026] The data store 140 can be a memory (e.g., random access memory), a drive (e.g., hard drive, flash drive), a database system, or another type of component or device capable of storing data. The data store 140 can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices (e.g., multiple server computers). The data store 140 can store data associated with processing of substrates on the manufacturing tool 124. For example, the data store 140 can store data (referred to as process data) collected by the sensors 126 of the manufacturing tool 124 before, during, or after the substrate process. Process data can refer to historical process data (e.g., process data generated for a previous substrate processed on the manufacturing system) and / or current process data (e.g., process data generated for a current substrate processed on the manufacturing system). The data store can also store spectral or non-spectral data associated with a portion of the substrate processed on the manufacturing tool 124. The spectral data can include historical spectral data and / or current spectral data.

[0027] The data store 140 may also store context data associated with one or more substrates processed in the manufacturing system. The context data may include recipe name, recipe step number, preventive maintenance indicator, operator, etc. The context data may refer to historical context data (e.g., context data associated with a previous process performed on a previous substrate) and / or current process data (e.g., context data associated with a current or future process performed on a previous substrate). The context data may further include identifying a sensor associated with a particular subsystem of a process chamber.

[0028] The data store 140 may also store task data. The task data may include one or more sets of operations to be performed on a substrate during a deposition process and may include one or more settings associated with each operation. For example, task data for a deposition process may include a temperature setting for a process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor of a material of a film to be deposited on a substrate, and the like. In another example, the task data may include controlling a pressure at a pressure point defined relative to a flow rate value. The task data may refer to past task data (e.g., task data associated with a previous process performed on a previous substrate) and / or current task data (e.g., task data associated with a current or future process to be performed on a substrate).

[0029] In some embodiments, the data store 140 can store an expected profile, a thickness profile, and a correction profile. The expected profile can include one or more data points associated with a desired film profile expected to be produced by a particular process recipe. In some embodiments, the expected profile can include a desired thickness of the film. The thickness profile can include one or more data points associated with a current film profile produced by the fabrication tool 124. The thickness profile can be measured using a metrology tool 127, 128. The correction profile can include one or more adjustments or offsets applied to parameters of the process chamber or process recipe. For example, the correction profile can include adjustments to a temperature setting of the process chamber, a pressure setting of the process chamber, a flow rate setting of a precursor of a material included in the film deposited on the substrate surface, a power supplied to the process chamber, a ratio of two or more settings, etc. The correction profile can be generated by comparing the expected profile (e.g., a thickness profile expected to be produced by the process recipe) and using a library of known failure patterns and / or algorithms to determine adjustments to be applied to parameters of the process recipe to achieve the expected profile. The correction profile can be applied to steps associated with a deposition process, an etch process, etc.

[0030] In some embodiments, data store 140 can be configured to store data that is not accessible to users of the manufacturing system. For example, process data, spectral data, contextual data, etc. acquired for a substrate being processed in the manufacturing system are not accessible to users (e.g., operators) of the manufacturing system. In some embodiments, all data stored in data store 140 can be made inaccessible to users of the manufacturing system. In other or similar embodiments, some of the data stored in data store 140 can be made inaccessible to users, while other portions of the data stored in data store 140 can be made accessible to users. In some embodiments, one or more portions of the data stored in data store 140 can be encrypted using an encryption mechanism not known to the user (e.g., the data is encrypted using a private encryption key). In other or similar embodiments, data store 140 can include multiple data stores, where data that is not accessible to users is stored in one or more first data stores and data that is accessible to users is stored in one or more second data stores.

[0031] In some embodiments, the data store 140 may be configured to store data associated with known failure patterns. A failure pattern may be one or more values ​​(e.g., vectors, scalars, etc.) associated with one or more problems or faults associated with the process chamber subsystem. In some embodiments, a failure pattern may be associated with a corrective action. For example, a failure pattern may include parameter adjustment steps to correct the problem or fault indicated by the failure pattern. For example, the predictive system may compare the determined failure pattern to a library of known failure patterns to determine the type of failure that occurred in the subsystem, the cause of the failure, the recommended corrective action to correct the failure, etc.

[0032] In some embodiments, prediction system 110 includes prediction server 112, server machine 170, and server machine 180. Prediction server 112, server machine 170, and server machine 180 may each include one or more computing devices, such as a rack mount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, a graphic processing unit (GPU), an accelerator application specific integrated circuit (ASIC) (e.g., a tensor processing unit (TPU)), etc.

[0033] The server machine 170 includes a training set generator 172 that can generate training datasets (e.g., a set of data inputs and a set of target outputs) for training, validating, and / or testing the machine learning model 190. The machine learning model 190 can be any algorithmic model capable of learning from data. Some operations of the dataset generator 172 are described in more detail below with respect to FIG. 2. In some embodiments, the dataset generator 172 can divide the training data into a training set, a validation set, and a test set. In some embodiments, the prediction system 110 generates multiple training datasets.

[0034] The server machine 180 may include a training engine 182, a validation engine 184, a selection engine 185, and / or a test engine 186. An engine may refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing unit, etc.), software (e.g., instructions executed on a processing unit, general-purpose computer system, or dedicated machine), firmware, microcode, or a combination thereof. The training engine 182 may train one or more machine learning models 190. The machine learning models 190 may refer to model artifacts created by the training engine 182 using training data (also referred to herein as a training set) that includes training inputs and corresponding target outputs (correct answers for each training input). The training engine 182 may find patterns in the training data that map the training inputs to the target outputs (predicted answers) and provide the machine learning models 190 that capture these patterns. The machine learning model 190 may use one or more of statistical modeling, support vector machines (SVM), radial basis functions (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithms (k-NN), linear regression, random forests, neural networks (e.g., artificial neural networks), and the like.

[0035] One type of machine learning model that can be used to perform some or all of the above tasks is an artificial neural network, such as a deep neural network. An artificial neural network generally includes a feature representation component with a classifier or regression layer that maps features to a desired output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters. Pooling may be performed and nonlinearities may be addressed in lower layers, on top of which a multi-layer perceptron is typically added to map the top layer features extracted by the convolutional layers to a decision (e.g., classification output). Deep learning is a class of machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep neural networks can be trained in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. Deep neural networks include a hierarchy of layers, with different layers learning different levels of representations corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract and complex representation. In plasma process tuning, for example, the raw input can be a process result profile (e.g., a thickness profile showing one or more thickness values ​​across the surface of a substrate), a second layer can constitute feature data associated with the state of one or more zones of a control element of a plasma processing system (e.g., zone orientation, plasma exposure time, etc.), and a third layer can include a starting recipe (e.g., a recipe used as a starting point to determine an updated process recipe for processing a substrate to produce a process result that meets a threshold criterion). Notably, a deep learning process can learn itself which features to optimally place at which level. The "deep" in "deep learning" refers to the number of layers through which data is transformed. More precisely, deep learning systems have a significant confidence assignment path (CAP) depth. A CAP is a chain of transformations from input to output.CAP describes the potential causal relationships between inputs and outputs. For feedforward neural networks, the depth of the CAP can be the depth of the network, which can be the number of hidden layers + 1. For recurrent neural networks, where signals can propagate through layers more than once, the depth of the CAP is potentially unlimited.

[0036] In one embodiment, the one or more machine learning models are recurrent neural networks (RNNs). RNNs are a type of neural network that includes memory that allows the neural network to capture temporal dependencies. RNNs can learn input-output mappings that depend on both current and past inputs. RNNs handle past and future flow measurements and make predictions based on this continuous measurement information. RNNs can be trained using a training data set to generate a fixed number of outputs (e.g., determining a set of substrate processing rates, determining modifications to a substrate process recipe). One type of RNN that can be used is a long short-term memory (LSTM) neural network.

[0037] Training of a neural network may be accomplished in a supervised learning fashion, which involves feeding a training dataset made up of labeled inputs through the network, observing its output, defining an error (by measuring the difference between the output and the label value), and using techniques such as deep gradient descent and backpropagation to adjust the network weights across all layers and nodes of the network so that the error is minimized. In many applications, repeating this process across many labeled inputs of the training dataset results in a network that can generate correct outputs even when presented with inputs that differ from those present in the training dataset.

[0038] A training data set including hundreds, thousands, tens of thousands, hundreds of thousands, or more sensor data and / or process result data (e.g., metrology data such as one or more thickness profiles associated with the sensor data) can be used to form the training data set.

[0039] To accomplish the training, the processing logic may input the training data set to one or more untrained machine learning models. Prior to inputting the first input to the machine learning models, the machine learning models may be initialized. The processing logic trains the untrained machine learning models based on the training data set to generate one or more trained machine learning models that perform various operations as described above. The training may be performed by inputting one or more of the sensor data to the machine learning models one at a time.

[0040] Machine learning models process inputs to generate outputs. An artificial neural network includes an input layer made up of values ​​of data points. The next layer is called a hidden layer, where each node in the hidden layer receives one or more input values. Each node includes parameters (e.g., weights) that it applies to the input values. Thus, each node essentially inputs the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to generate an output value. The next layer can be another hidden layer or an output layer. In either case, the nodes in the next layer receive output values ​​from the nodes in the previous layer, and each node applies weights to these values ​​and then generates its own output value. This can be done at each layer. The final layer is the output layer, where there is one node for each class, prediction, and / or output that the machine learning model can generate.

[0041] Thus, the output may include one or more predictions or inferences. For example, the output predictions or inferences may include one or more predictions of film build-up on the chamber parts, corrosion of the chamber parts, predicted failure of the chamber parts, etc. The processing logic determines an error (i.e., classification error) based on a difference between the output (e.g., prediction or inference) of the machine learning model and a target label associated with the input training data. The processing logic adjusts weights of one or more nodes of the machine learning model based on the error. An error term or delta may be determined for each node of the artificial neural network. Based on this error, the artificial neural network adjusts one or more of its parameters (weights of one or more inputs of the node) of one or more of its nodes. The parameters may be updated in a back-propagation fashion, with the nodes in the top layer updated first, followed by the nodes in the next layer, and so on. The artificial neural network includes multiple layers of "neurons," each layer receiving as inputs values ​​from the neurons in the previous layer. The parameters of each neuron include weights associated with values ​​received from each of the neurons in the previous layer. Thus, adjusting the parameters may include adjusting weights assigned to each of the inputs of one or more neurons in one or more layers within the artificial neural network.

[0042] After one or more training rounds, the processing logic can determine whether a stopping criterion has been met. The stopping criterion can be a target level of accuracy, a target number of processed images from the training dataset, a target amount of change to a parameter over one or more previous data points, combinations thereof, and / or other criteria. In one embodiment, the stopping criterion is met when at least a minimum number of data points have been processed and at least a threshold accuracy has been achieved. The threshold accuracy can be, for example, 70%, 80%, or 90% accuracy. In one embodiment, the stopping criterion is met when the accuracy of the machine learning model stops improving. If the stopping criterion is not met, further training is performed. If the stopping criterion is met, the training can be completed. Once the machine learning model is trained, the model can be tested using a reserved portion of the training dataset.

[0043] Once one or more trained machine learning models 190 are generated, these models may be stored in the prediction server 112 as a prediction component 114 or as components of the prediction component 114.

[0044] The validation engine 184 may be capable of validating the machine learning model 190 using the corresponding set of features of the validation set from the training set generator 172. Once the model parameters are optimized, model validation may be performed to determine if the model has been improved and to determine the current accuracy of the deep learning model. The validation engine 184 may determine the accuracy of the machine learning model 190 based on the corresponding set of features of the validation set. The validation engine 184 may discard the trained machine learning models 190 that have an accuracy that does not meet the threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting the trained machine learning model 190 that has an accuracy that meets the threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting the trained machine learning model 190 that has the highest accuracy among the trained machine learning models 190.

[0045] The testing engine 186 may be able to test the trained machine learning models 190 using a corresponding set of features of a test set from the dataset generator 172. For example, a first trained machine learning model 190 trained using a first set of features of the training set may be tested using a first set of features of the test set. The testing engine 186 may determine the trained machine learning model 190 that has the highest accuracy of all the trained machine learning models based on the test set.

[0046] As described in more detail below, the prediction server 112 provides data indicative of the expected behavior of each subsystem of the process chamber and includes a prediction component 114 that can execute trained machine learning models 190 against current sensor data inputs to obtain one or more outputs. The prediction server 112 can further provide data indicative of the health and diagnostics of the process chamber subsystems, as described in more detail below.

[0047] The client devices 120, the manufacturing equipment 124, the sensors 126, the metrology equipment 128, the prediction server 112, the data store 140, the server machine 170, and the server machine 180 can be coupled to one another via a network 130. In some embodiments, the network 130 is a public network that provides the client devices 120 with access to the prediction server 112, the data store 140, and other publicly available computing devices. In some embodiments, the network 130 is a private network that provides the client devices 120 with access to the manufacturing equipment 124, the metrology equipment 128, the data store 140, and other privately available computing devices. The network 130 can include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), routers, hubs, switches, server computers, cloud computing networks, and / or combinations thereof.

[0048] It should be noted that in some other implementations, the functionality of server machines 170 and 180 and prediction server 112 may be provided by a fewer number of machines. For example, in some embodiments, server machines 170 and 180 may be combined into a single machine, while in some other or similar embodiments, server machines 170 and 180 and prediction server 112 may be combined into a single machine.

[0049] In general, functionality described in one embodiment as being performed by server machine 170, server machine 180, and / or prediction server 112 may also be performed on client device 120. Additionally, functionality attributed to a particular component may be performed by different components or multiple components working together.

[0050] In an embodiment, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass a "user" being an entity controlled by multiple users and / or automated sources. For example, a collection of individual users federated as a group of administrators may be considered a "user."

[0051] 2 is a flow diagram of a method 200 for training a machine learning model according to an aspect of the present disclosure. The method 200 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, the method 200 may be performed by a computer system such as the computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of the method 200 may be performed by one or more other machines not shown in the figure. In some aspects, one or more operations of the method 200 may be performed by the server machine 170, the server machine 180, and / or the prediction server 112.

[0052] For ease of explanation, the method is shown and described as a series of acts. However, acts according to the present disclosure may be performed in various orders and / or simultaneously, as well as with other acts not shown and described herein. Moreover, not all acts shown may be performed to implement a method in accordance with the disclosed subject matter. Furthermore, those skilled in the art will understand and appreciate that a method may alternatively be represented as a series of interrelated states, via a state diagram or events. Furthermore, it should be appreciated that the methods disclosed herein may be stored in an article of manufacture to facilitate transport and transfer of such methods to a computing device. The term article of manufacture as used herein is intended to encompass a computer program accessible from any computer-readable device or storage medium.

[0053] At block 210, processing logic initializes a training set T to an empty set (e.g., {}).

[0054] At block 212, the processing logic obtains sensor data (e.g., sensor values, characteristics, trace data) associated with a previous deposition process performed to deposit one or more layers of a film on a surface of a previous substrate. The sensor data may be further associated with subsystems of the process chamber. A subsystem may be characterized as a set of sensors related to an operational parameter of the process chamber. The operational parameter may be temperature, flow rate, pressure, etc. For example, a pressure subsystem may be characterized by one or more sensors measuring gas flow, chamber pressure, control valve angle, foreline (vacuum line between pumps) pressure, pump speed, etc. Each process chamber may include multiple different subsystems, such as a pressure subsystem, a flow rate subsystem, a temperature subsystem, etc.

[0055] In some embodiments, the sensor data associated with the deposition process is historical data associated with one or more previous deposition settings for a previous deposition process previously performed on a previous substrate in the manufacturing system. For example, the historical data can be historical context data associated with a previous deposition process stored in the data store 140. In some embodiments, the one or more previous deposition settings can include at least one of a previous temperature setting for the previous deposition process, a previous pressure setting for the previous deposition setting, a previous flow rate setting for a precursor for one or more materials of a previous film deposited on the surface of the previous substrate, or any other setting associated with the deposition process. The flow rate setting can refer to a flow rate setting of a precursor in an initial stage of a previous deposition process (referred to as an initial flow rate setting), a flow rate setting of a precursor in a final stage of a previous deposition process (referred to as a final flow rate setting), or a ramp rate of a flow rate of a precursor during a deposition process. In one example, the precursor of the previous film can include a boron-containing precursor or a silicon-containing precursor. In some embodiments, the sensor data can also be associated with a previous etching process performed on a previous substrate, or any other process performed in the process chamber.

[0056] At block 214, processing logic obtains metrology data associated with the film deposited on the surface of the previous substrate. The metrology data may include values ​​such as film property data (e.g., wafer spatial film properties), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. The metrology data may be associated with or indicative of hardware parameters such as settings or components (e.g., size, type, etc.) of the manufacturing equipment, or manufacturing parameters such as process parameters of the manufacturing equipment. In one example, the metrology data may include thickness data (e.g., thickness profile) associated with the film deposited on the surface of the previous substrate. The thickness data may be correlated to a particular step associated with the process recipe. The processing logic may obtain the thickness data from the data store 140 according to the above-described embodiment.

[0057] At block 216, processing logic generates first training data based on acquired sensor data associated with a previous deposition process performed on a previous substrate, and at block 218, processing logic generates second training data based on metrology data associated with a recipe for a film deposited on the surface of the previous substrate.

[0058] At block 220, processing logic generates a mapping between the first training data and the second training data, where the first training data includes or is based on data of a previous deposition process performed on a previous substrate, and the second training data includes or is based on thickness data associated with a recipe for a film deposited on a surface of the previous substrate, and the first training data is associated with (or mapped to) the second training data. At block 224, processing logic adds the mapping to the training set T.

[0059] At block 226, the processing logic determines whether the training set T includes a sufficient amount of training data to train the machine learning model. Note that in some implementations, the sufficiency of the training set T can be determined simply based on the number of mappings in the training set, while in some other implementations, the sufficiency of the training set T can be determined based on one or more other criteria (e.g., a measure of diversity of training examples, etc.) in addition to or instead of the number of input / output mappings. In response to determining that the training set does not include a sufficient amount of training data to train the machine learning model, the method 200 returns to block 212. In response to determining that the training set T includes a sufficient amount of training data to train the machine learning model, the method 200 continues to block 228.

[0060] At block 228, the processing logic provides a training set T for training the machine learning model. In one embodiment, the training set T is provided to the training engine 182 of the server machine 180 for training. In the case of a neural network, for example, input values ​​of a given input / output mapping are input to the neural network, and output values ​​of the input / output mapping are stored in the output nodes of the neural network. Then, the connection weights in the neural network are adjusted according to a learning algorithm (e.g., backpropagation, etc.), and this procedure is repeated for other input / output mappings in the training set T.

[0061] In some embodiments, the processing logic may perform outlier detection methods to remove anomalies from the training set T prior to training the machine learning model. Outlier detection methods may include techniques to identify values ​​that are significantly different from the majority of the training data. These values ​​may be generated from errors, noise, etc.

[0062] After block 228, the machine learning model can be used to generate one or more values ​​indicative of a failure pattern (e.g., abnormal thickness) in the deposited film, generate predictive data indicative of the type of corrective action to be taken, and / or perform corrective action to correct the suspected problem or failure. Predictive data can be generated by comparing the failure pattern to a library of known failure patterns.

[0063] In some embodiments, the manufacturing system may include more than one process chamber. For example, the exemplary manufacturing system 300 of FIG. 3 shows multiple process chambers 314, 316, 318. It should be noted that in some embodiments, the data acquired to train the machine learning model and the data collected to be provided as input to the machine learning model may be associated with the same process chamber of the manufacturing system. In other or similar embodiments, the data acquired to train the machine learning model and the data collected to be provided as input to the machine learning model may be associated with different process chambers of the manufacturing system. In other or similar embodiments, the data acquired to train the machine learning model may be associated with a process chamber of a first manufacturing system, and the data collected to be provided as input to the machine learning model may be associated with a process chamber of a second manufacturing system.

[0064] 3 is a top schematic view of an exemplary manufacturing system 300 according to an embodiment of the present disclosure. The manufacturing system 300 can perform one or more processes on a substrate 302. The substrate 302 can be any suitable rigid, planar article of fixed dimensions, such as, for example, a silicon-containing disk or wafer, a patterned wafer, a glass plate, etc., suitable for manufacturing electronic devices or circuit components.

[0065] The manufacturing system 300 can include a process tool 304 and a factory interface 306 coupled to the process tool 304. The process tool 304 can include a housing 308 having a transfer chamber 310 therein. The transfer chamber 310 can include one or more process chambers (also referred to as processing chambers) 314, 316, 318 disposed about and coupled to it. The process chambers 314, 316, 318 can be coupled to the transfer chamber 310 via respective ports, such as slit valves. The transfer chamber 310 can also include a transfer chamber robot 312 configured to transfer the substrate 302 between the process chambers 314, 316, 318, a load lock 320, or the like. The transfer chamber robot 312 can include one or more arms, each arm including one or more end effectors at the end of each arm. The end effectors can be configured to handle a particular object, such as a wafer, a sensor disk, a sensor tool, or the like.

[0066] The process chambers 314, 316, 318 can be adapted to perform any number of processes on the substrate 302. The same or different substrate processes can be performed in each process chamber 314, 316, 318. The substrate processes can include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, and the like. Other processes can be performed on the substrate in the process chamber. Each process chamber 314, 316, 318 can include one or more sensors configured to capture data of the substrate 302 before, after, or during the substrate process. For example, the one or more sensors can be configured to capture spectral and / or non-spectral data of a portion of the substrate 302 during the substrate process. In other or similar embodiments, the one or more sensors can be configured to capture data associated with the environment in the process chamber 314, 316, 318 before, after, or during the substrate process. For example, the one or more sensors can be configured to capture data associated with the temperature, pressure, gas concentrations, etc. of the environment within the process chambers 314, 316, 318 during substrate processing. In some embodiments, the process chambers 314, 316, 318 can include a metrology device 340.

[0067] The metrology tool 340 may be similar or the same as the metrology tools 127, 128. In some embodiments, the metrology tool 340 may be located within a process tool as shown. In other embodiments, the metrology tool 340 may be located within one or more process chambers 314, 316, 318. In some embodiments, the transfer chamber robot 312 may be used to place the substrate on the metrology tool 340. In other embodiments, the metrology tool 340 may be part of a substrate support assembly (not shown).

[0068] A load lock 320 may also be coupled to the housing 308 and the transfer chamber 310. The load lock 320 may be configured to interface with and couple to the transfer chamber 310 on one side, and the factory interface 306. The load lock 320 may have an environmentally controlled atmosphere that may be varied in some embodiments from a vacuum environment (substrates may be transferred to and from the transfer chamber 310) to an inert gas environment at or near atmospheric pressure (substrates may be transferred to and from the factory interface 306). The factory interface 306 may be any suitable enclosure, such as, for example, an Equipment Front End Module (EFEM). The factory interface 306 may be configured to receive substrates 302 from substrate carriers 322 (e.g., Front Opening Unified Pods (FOUPs)) docked to various load ports 324 of the factory interface 306. A factory interface robot 326 (shown in dotted lines) may be configured to transfer substrates 302 between the carriers (also called containers) 322 and the load lock 320. The carrier 322 may be a substrate storage carrier or a replacement part storage carrier.

[0069] The manufacturing system 300 may also be connected to a client device (not shown) configured to provide information regarding the manufacturing system 300 to a user (e.g., an operator). In some embodiments, the client device may provide information to a user of the manufacturing system 300 via one or more graphical user interfaces (GUIs). For example, the client device may provide information via the GUI regarding a target thickness profile of a film to be deposited on the surface of the substrate 302 during a deposition process performed in the process chambers 314, 316, 318. The client device may also provide information regarding modifications of the process recipe given a respective set of deposition settings that are predicted to correspond to the target profile in accordance with embodiments described herein.

[0070] The manufacturing system 300 may also include a system controller 328. The system controller 328 may be and / or may include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, etc. The system controller 328 may include one or more processing devices, which may be a general-purpose processing device, such as a microprocessor, a central processing unit, etc. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or a combination of instruction sets. The processing device may also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. The system controller 328 may include a data storage device (e.g., one or more disk drives and / or solid state drives), a main memory, a static memory, a network interface, and / or other components. The system controller 328 may execute instructions to perform any one or more of the methods and / or embodiments described herein. In some embodiments, the system controller 328 may execute instructions to perform one or more operations in the manufacturing system 300 in accordance with the process recipe. The instructions may be stored in a computer-readable storage medium, which may include a main memory, a static memory, a secondary storage device, and / or a processor (during execution of the instructions).

[0071] The system controller 328 may receive data from sensors contained on or within various portions of the manufacturing system 300 (e.g., processing chambers 314, 316, 318, transfer chamber 310, load lock 320, etc.). In some embodiments, the data received by the system controller 328 may include spectral and / or non-spectral data for a portion of the substrate 302. In other or similar embodiments, the data received by the system controller 328 may include data associated with processing the substrate 302 in the processing chambers 314, 316, 318, as previously described. For purposes of this description, the system controller 328 is described as receiving data from sensors contained within the process chambers 314, 316, 318. However, the system controller 328 may receive data from any portion of the manufacturing system 300 and use the data received from that portion in accordance with the embodiments described herein. In an illustrative example, the system controller 328 may receive data from one or more sensors in the process chambers 314, 316, 318 before, after, or during substrate processing in the process chambers 314, 316, 318. The data received from the sensors in various portions of the manufacturing system 300 may be stored in a data store 350. The data store 350 may be included as a component within the system controller 328 or may be a separate component from the system controller 328. In some embodiments, the data store 350 may be the data store 140 described with respect to FIG. 1.

[0072] FIG. 4 is a cross-sectional schematic side view of a process chamber 400 according to an embodiment of the present disclosure. In some embodiments, the process chamber 400 can correspond to the process chambers 314, 316, 318 described with respect to FIG. 3. The process chamber 400 can be used for a process in which a corrosive plasma environment is provided. For example, the process chamber 400 can be a chamber for a plasma etcher or a plasma etch reactor, or the like. In another example, the process chamber can be a chamber for a deposition process, as previously described. In one embodiment, the process chamber 400 includes a chamber body 402 and a showerhead 430 that surrounds an interior volume 406. The showerhead 430 can include a showerhead base and a showerhead gas distribution plate. Alternatively, the showerhead 430 can be replaced by a lid and a nozzle in some embodiments, or by multiple pie-shaped showerhead sections and plasma generation units in other embodiments. The chamber body 402 can be manufactured from aluminum, stainless steel, or other suitable materials, such as titanium (Ti). The chamber body 402 generally includes a sidewall 408 and a bottom 410. An exhaust port 426 may be defined in the chamber body 402 and may couple the interior volume 406 to a pumping system 428. The pumping system 428 may include one or more pumps and a throttle valve utilized to evacuate and regulate the pressure of the interior volume 406 of the process chamber 400.

[0073] The showerhead 430 can be supported on a sidewall 408 of the chamber body 402. The showerhead 420 (or lid) can be opened to allow access to the interior volume 406 of the process chamber 400 and can provide a seal to the process chamber 400 while closed. A gas panel 458 can be coupled to the process chamber 400 to supply process gases and / or cleaning gases to the interior volume 406 through the showerhead 430 or the lid and nozzles (e.g., through apertures in the showerhead or the lid and nozzles). For example, the gas panel 458 can provide precursors of a material of the film 451 to be deposited on the surface of the substrate 302. In some embodiments, the precursors can include silicon-based precursors or boron-based precursors. The showerhead 430 can include a gas distribution plate (GDP) and can have multiple gas supply holes 432 (also referred to as channels) throughout the GDP. A substrate support assembly 448 is disposed within the interior volume 406 of the process chamber 400 below the showerhead 430. The substrate support assembly 448 uses, for example, an electrostatic chuck 450 to hold the substrate 302 during processing (eg, during a deposition process).

[0074] In some embodiments, the processing chamber 400 may include a metrology device (not shown) configured to generate in situ metrology measurements during a process performed in the processing chamber 400. The metrology device may be operably coupled to a system controller (e.g., system controller 328 as described above). In some embodiments, the metrology device may be configured to generate metrology measurements (e.g., thickness) of the film 451 during a particular instance of the deposition process. The system controller may generate a thickness profile of the film 451 based on the metrology measurements received from the metrology device. In other or similar embodiments, the processing chamber 400 does not include a metrology device. In such embodiments, the system controller may receive one or more metrology measurements of the film 451 after completion of a deposition process in the process chamber 400. The system controller may determine a deposition rate based on the one or more metrology measurements and may associate and generate a thickness profile of the film 451 based on the determined concentration gradient and the determined deposition rate of the deposition process.

[0075] 5 is a flow diagram of a method 500 for determining a correction profile of a process recipe according to an embodiment of the present disclosure. The method 500 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or any combination thereof. In one embodiment, the method 500 may be performed by a computer system such as the computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of the method 500 may be performed by one or more other machines not shown in the figure. In some embodiments, one or more operations of the method 500 may be performed by a manufacturing apparatus 124 and / or a client device 122.

[0076] In operation 510, processing logic performs a deposition process on the substrate according to a process recipe. The deposition process may be performed in one or more process chambers. The process recipe may include one or more setting parameters for the deposition process. For example, the setting parameters may include a temperature setting for the process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor of a material included in the film to be deposited on the substrate surface, a showerhead height, etc. The deposition process may deposit multiple layers on the substrate. For example, the deposition process may deposit alternating layers of oxide and nitride layers, alternating layers of oxide and polysilicon layers, etc.

[0077] In operation 512, the processing logic generates metrology data associated with the deposited film. The metrology data can include values ​​of film property data (e.g., wafer spatial film properties), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. In some embodiments, the metrology data can include a thickness profile indicative of the thickness of one or more layers of the film. The thickness profile can include a number of data values ​​indicative of the thickness of the deposited film at different locations on the substrate. In an illustrative example, the thickness profile can include approximately 3,000 data values ​​across the surface of the substrate. In another embodiment, the thickness profile can include only data values ​​along the edge of the substrate, which can be used to generate a correction profile to improve the etching process along the edge of the deposited film. In some embodiments, the metrology data can be generated using an in situ metrology tool (e.g., metrology tool 127 or metrology tool 340) located in the process chamber performing the deposition process or in the transfer chamber.

[0078] In operation 514, processing logic generates a correction profile based on the metrology data. The correction profile may include one or more corrective actions applied to a process recipe or a parameter of a process chamber. For example, the correction profile may include adjustments to a temperature setting of the process chamber, a pressure setting of the process chamber, a flow rate setting of a precursor of a material included in a film deposited on the substrate surface, a power supplied to the process chamber, a ratio of two or more settings, etc. In some embodiments, processing logic may generate the correction profile by determining a delta (difference) between an expected profile (e.g., a thickness profile expected to be produced by a process recipe) and a thickness profile produced by a metrology tool. Processing logic may then generate the correction profile by applying an algorithm to this delta and / or by searching for the delta in a library of known faults. For example, the library may include a list of deltas that correlate to a particular correction profile.

[0079] In some embodiments, the correction profile can be generated using a machine learning model (e.g., model 190). For example, the thickness profile can be input into a trained machine learning model. The trained machine learning model can output a value indicative of the difference between an expected thickness profile of the film on the substrate and an actual thickness profile of the film on the substrate. The processing logic can then compare the output to a library of known fault patterns to determine the correction profile. In another example, the machine learning model can output one or more values ​​indicative of the correction profile.

[0080] In operation 516, processing logic generates an updated process recipe by applying the correction profile to the process recipe. For example, the correction profile may be applied to one or more steps of a current deposition process, an etching process performed on the deposited film, or any other process of the recipe (e.g., a polishing process, a cleaning process, etc.).

[0081] In operation 518, processing logic performs a subsequent step of the process recipe on the substrate according to the updated process recipe. In some embodiments, the subsequent step includes another deposition step, an etching process, a polishing process, a cleaning process, etc. In some embodiments, the updated process recipe can be used to perform an additional deposition step on the substrate. For example, processing logic can deposit a first set of film layers on the substrate, determine a thickness profile of the deposited film, generate a correction profile to correct for any disturbances detected during deposition of the first set of film layers, apply the correction profile to the process recipe, and deposit a second set of film layers on the substrate. Thus, the deposition process recipe can be adjusted in real time or near real time.

[0082] In some embodiments, the updated process recipe can be used to perform an etch process on the deposited film layer. For example, processing logic can determine a thickness profile of the deposited film and generate a correction profile based on the difference between the expected thickness and the actual thickness of the film on the substrate. Processing logic can then apply the correction profile to the process recipe to adjust the etch process of the recipe. It should be understood that the etch process need not be associated with the same recipe as the deposition process, but can be associated with a different recipe. In an exemplary embodiment, the correction profile can perform corrections to the etch process to improve etching of the edge of the substrate, improve or eliminate process drifts during the etch process (e.g., chuck temperature drift, lid temperature change, coating degradation, etc.), etc.

[0083] 6 is a flow diagram of a method 600 for adjusting a process recipe according to an embodiment of the present disclosure. The method 600 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or any combination thereof. In one embodiment, the method 600 may be performed by a computer system such as the computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of the method 600 may be performed by one or more other machines not shown in the figure. In some embodiments, one or more operations of the method 600 may be performed by a manufacturing apparatus 124 and / or a client device 122.

[0084] In operation 610, processing logic performs a deposition process on the substrate according to a process recipe. The deposition process may occur in a first process chamber. The process recipe may include one or more setting parameters for the deposition process.

[0085] In operation 612, processing logic generates metrology data associated with the deposited film. The metrology data may include a thickness profile indicative of a thickness of one or more layers of the film. The metrology data may be generated using an in situ metrology tool (e.g., metrology tool 127 or metrology tool 340) located in a process chamber performing the deposition process or in a transfer chamber.

[0086] In operation 614, processing logic generates a correction profile based on the metrology data. The correction profile can include one or more corrective actions applied to a process recipe or a parameter of a process chamber. For example, the correction profile can include adjustments to a temperature setting of the process chamber, a pressure setting of the process chamber, a flow rate setting for a precursor of a material included in a film deposited on the substrate surface, a power supplied to the process chamber, a ratio of two or more settings, etc.

[0087] In operation 616, the processing logic performs a chamber matching operation on the second process chamber based on the correction profile. Chamber matching can be used to minimize process variations between substrates due to hardware variations. For example, the hardware variations can include different chuck temperatures, different lid temperatures, differences in power supply to each process chamber, differences in gas flows and / or pressures, etc. The chamber matching operation can include adjusting one or more parameters of the second process chamber or adjusting one or more parameters of a recipe used to generate the substrate in the second process chamber.

[0088] 7 is a flow diagram of a method 700 for determining a correction profile using a machine learning model according to an embodiment of the present disclosure. The method 700 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, the method 700 can be performed by a computer system such as the computer system architecture 100 of FIG. 1. In other or similar implementations, one or more operations of the method 700 can be performed by one or more other machines not shown in the figure. In some aspects, one or more operations of the method 700 can be performed by the server machine 170, the server machine 180, and / or the prediction server 112.

[0089] At block 710, processing logic obtains metrology data associated with operations performed in the process chamber. In some embodiments, the operations may include a deposition process performed in the process chamber to deposit one or more layers of a film on a surface of a substrate, an etching process performed on one or more layers of a film on the surface of a substrate, etc. The operations may be performed according to a recipe. The metrology data may include values ​​of film property data (e.g., wafer spatial film properties), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. The metrology data may be associated with or indicative of hardware parameters, such as settings or components (e.g., size, type, etc.) of a manufacturing equipment, or manufacturing parameters, such as process parameters of the manufacturing equipment.

[0090] At block 712, processing logic applies a machine learning model (e.g., model 190) to the acquired metrology data. The machine learning model may be used to generate one or more values ​​indicative of a delta between an expected thickness profile of the film on the substrate and an actual thickness profile of the film on the substrate, or one or more values ​​indicative of a correction profile. For example, the machine learning model may use an algorithm to generate the delta or correction profile using a training set T.

[0091] At block 714, the processing logic generates an output via the machine learning model based on the metrology data. In some embodiments, the output can be a value indicative of a pattern (e.g., a correction profile, a fault pattern). In particular, the output can include prediction data of a corrective action to be applied to the process recipe. In some embodiments, the output can be at least one value indicative of a delta between an expected thickness profile of the film on the substrate and an actual thickness profile of the film on the substrate. In another embodiment, the output can be a correction profile to be applied to the process recipe.

[0092] At block 716, the processing logic may perform (or suggest) a corrective action based on the output. In some embodiments, the corrective action may be determined based on data obtained from a fault library. In some embodiments, the corrective action may include generating an alert or indication of the determined problem to the client device 120. In some embodiments, the corrective action may include the processing logic adjusting one or more parameters of the deposition process recipe, the etch process recipe, or any other process recipe (e.g., a temperature setting of a process chamber, a pressure setting of a process chamber, a flow rate setting of a precursor of a material included in the film deposited on the substrate surface, etc.) based on the desired properties of the film. In some embodiments, the process recipe may be adjusted before, during (e.g., in real time), or after completion of the process recipe.

[0093] FIG. 8 is a block diagram illustrating a computer system 800 according to certain embodiments. In some embodiments, computer system 800 may be connected to other computer systems (e.g., via a network such as a local area network (LAN), an intranet, an extranet, or the Internet). Computer system 800 may operate in the capacity of a server or a client computer in a client-server environment, or as a peer computer in a peer-to-peer or distributed network environment. Computer system 800 may be provided by a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a server, a network router, a switch or bridge, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Additionally, the term "computer" is intended to include any collection of computers that individually or jointly execute a set (or sets) of instructions to perform any one or more of the methods described herein.

[0094] In a further aspect, the computer system 800 may include a processing unit 802, a volatile memory 804 (e.g., random access memory (RAM)), a non-volatile memory 806 (e.g., read only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and a data storage device 816, which may communicate with each other via a bus 808.

[0095] The processing unit 802 may be provided by one or more processors, such as a general-purpose processor (e.g., a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a combination of multiple types of instruction sets, etc.) or a special-purpose processor (e.g., an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor, etc.).

[0096] Computer system 800 may further include a network interface device 822 (e.g., coupled to a network 874). Computer system 800 may also include a video display unit 810 (e.g., LCD), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse), and a signal generating device 820.

[0097] In some implementations, the data storage device 816 may include a non-transitory computer-readable storage medium 824 capable of storing instructions 826 encoding any one or more of the methods or functions described herein, including instructions for encoding the components of FIG. 1 (e.g., the corrective action component 122, the prediction component 114, etc.) and implementing the methods described herein.

[0098] The instructions 826 may also reside, completely or partially, within the volatile memory 804 and / or within the processing unit 802 during execution thereof by the computer system 800; and thus, the volatile memory 804 and the processing unit 802 may also constitute machine-readable storage media.

[0099] Although the computer-readable storage medium 824 is shown as a single medium in the illustrative example, the term "computer-readable storage medium" is intended to include a single medium or multiple media (e.g., centralized or distributed databases and / or associated caches and servers) that store one or more sets of executable instructions. The term "computer-readable storage medium" is also intended to include any tangible medium capable of storing or encoding a set of instructions for execution by a computer that causes the computer to perform any one or more of the methods described herein. The term "computer-readable storage medium" is intended to include, but is not limited to, solid-state memory, optical media, and magnetic media.

[0100] The methods, components, and features described herein may be implemented by discrete hardware components or may be integrated into the functionality of other hardware components, such as ASICs, FPGAs, DSPs, or similar devices. Additionally, the methods, components, and features may be implemented by firmware modules or functional circuits within a hardware device. Additionally, the methods, components, and features may be implemented in any combination of hardware devices and computer program components, or in a computer program.

[0101] Unless otherwise indicated, terms such as "receive," "perform," "provide," "obtain," "cause," "access," "determine," "add," "use," "train," and the like refer to computer system-performed or implemented actions and processes that manipulate and transform data represented as physical (electronic) quantities in the computer system's registers and memory into other data similarly represented as physical quantities in the computer system's memory or registers, or other such information storage, transmission, or display devices. Also, terms such as "first," "second," "third," "fourth," and the like, as used herein, are intended as labels to distinguish between different elements and cannot have a sequential meaning due to their numerical designations.

[0102] The examples described herein also relate to an apparatus for carrying out the methods described herein. The apparatus may be specially constructed to carry out the methods described herein, or may include a general-purpose computer system selectively programmed by a computer program stored in the computer system. Such a computer program may be stored in a computer-readable tangible storage medium.

[0103] The methods and illustrative embodiments described herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used in accordance with the teachings described herein, or it may prove convenient to construct more specialized apparatus to perform the methods described herein and / or each of their individual functions, routines, subroutines, or operations. Examples of structures for a variety of these systems are set forth in the description above.

[0104] The above description is intended to be illustrative and not limiting. Although the present disclosure has been described with reference to certain exemplary examples and embodiments, it will be recognized that the present disclosure is not limited to the described examples and embodiments. The scope of the present disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. acquiring metrology data generated using a metrology device located within a process chamber performing a deposition process on a substrate according to a process recipe, the process recipe including a plurality of set parameters, the deposition process producing a plurality of film layers on a surface of the substrate; generating a correction profile based on the metrology data; generating an updated process recipe by applying the correction profile to the process recipe; performing an etch process on the substrate in accordance with the updated process recipe; A method comprising:

2. The method of claim 1 , wherein the metrology data indicates a thickness of one or more of the film layers.

3. The method of claim 1 , wherein the correction profile comprises one or more corrective actions applied to the plurality of set parameters of the process recipe.

4. generating the correction profile, determining a delta between an expected thickness profile associated with the process recipe and a thickness profile generated by the metrology device; applying an algorithm to the delta; The method of claim 1 , comprising:

5. generating the correction profile, determining a delta between an expected thickness profile associated with the process recipe and a thickness profile generated by the metrology device; performing a search for said delta in a library of known faults, said library containing a list of delta values ​​that correlate to a particular correction profile; The method of claim 1 , comprising:

6. generating the correction profile, inputting the metrology data into a trained machine learning model; obtaining an output value of the trained machine learning model, the output value indicative of a difference between an expected thickness profile of the film on the substrate and an actual thickness profile of the film on the substrate; performing a search for said delta in a library of known faults, said library containing a list of delta values ​​that correlate to a particular correction profile; The method of claim 1 , comprising:

7. generating the correction profile, inputting the metrology data into a trained machine learning model; obtaining an output value of the trained machine learning model, the output value being indicative of a correction profile; The method of claim 1 , comprising:

8. performing a chamber matching operation on another process chamber based on the correction profile. The method of claim 1 further comprising:

9. A transfer chamber; a process chamber coupled to the transfer chamber; a metrology device located within at least one of the transfer chamber or the process chamber, the manufacturing system comprising: acquiring metrology data generated using the metrology device located within the process chamber performing a deposition process on a substrate according to a process recipe, the process recipe including a plurality of set parameters, the deposition process producing a plurality of film layers on a surface of the substrate; generating a correction profile based on the measurement data; generating an updated process recipe by applying the correction profile to the process recipe; performing an etch process on the substrate according to the updated process recipe; It is configured as follows: Electronic device manufacturing systems.

10. The electronic device manufacturing system of claim 9 , wherein the metrology data indicates a thickness of one or more of the film layers.

11. The electronic device manufacturing system of claim 9 , wherein the correction profile comprises one or more corrective actions applied to the plurality of setting parameters of the process recipe.

12. generating the correction profile, determining a delta between an expected thickness profile associated with the process recipe and a thickness profile generated by the metrology device; applying an algorithm to the delta; The electronic device manufacturing system of claim 9 .

13. generating the correction profile, determining a delta between an expected thickness profile associated with the process recipe and a thickness profile generated by the metrology device; performing a search for said delta in a library of known faults, said library containing a list of delta values ​​that correlate to a particular correction profile; The electronic device manufacturing system of claim 9 .

14. generating the correction profile, inputting the metrology data into a trained machine learning model; obtaining an output value of the trained machine learning model, the output value indicative of a difference between an expected thickness profile of the film on the substrate and an actual thickness profile of the film on the substrate; performing a search for said delta in a library of known faults, said library containing a list of delta values ​​that correlate to a particular correction profile; The electronic device manufacturing system of claim 9 .

15. generating the correction profile, inputting the metrology data into a trained machine learning model; obtaining an output value of the trained machine learning model, the output value being indicative of a correction profile; The electronic device manufacturing system of claim 9 .

16. Performing a chamber matching operation on another process chamber based on the correction profile.

10. The electronic device manufacturing system of claim 9, further configured to:

17. A non-transitory computer-readable storage medium containing instructions that, when executed by a processor operatively coupled to a memory, acquiring metrology data generated using a metrology device located within a process chamber performing a deposition process on a substrate according to a process recipe, the process recipe including a plurality of set parameters, the deposition process producing a plurality of film layers on a surface of the substrate; generating a correction profile based on the metrology data; generating an updated process recipe by applying the correction profile to the process recipe; performing an etch process on the substrate in accordance with the updated process recipe; 16. A non-transitory computer-readable storage medium for performing operations including:

18. generating the correction profile, determining a delta between an expected thickness profile associated with the process recipe and a thickness profile generated by the metrology device; applying an algorithm to the delta; 20. The non-transitory computer-readable storage medium of claim 17, comprising:

19. generating the correction profile, determining a delta between an expected thickness profile associated with the process recipe and a thickness profile generated by the metrology device; performing a search for said delta in a library of known faults, said library containing a list of delta values ​​that correlate to a particular correction profile; 20. The non-transitory computer-readable storage medium of claim 17, comprising:

20. generating the correction profile, inputting the metrology data into a trained machine learning model; obtaining an output value of the trained machine learning model, the output value indicative of a difference between an expected thickness profile of the film on the substrate and an actual thickness profile of the film on the substrate; performing a search for said delta in a library of known faults, said library containing a list of delta values ​​that correlate to a particular correction profile; 20. The non-transitory computer-readable storage medium of claim 17, comprising:

21. generating the correction profile, inputting the metrology data into a trained machine learning model; obtaining an output value of the trained machine learning model, the output value being indicative of a correction profile; 20. The non-transitory computer-readable storage medium of claim 17, comprising:

22. The processing device comprises: performing a chamber matching operation on another process chamber based on the correction profile.

20. The non-transitory computer-readable storage medium of claim 17, further configured to:

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