Methods and mechanisms for adjusting film deposition parameters during substrate manufacturing

Abstract

To provide a method for automatically adjusting film deposition parameters, an electronic device manufacturing system, and a non-transitory computer-readable storage medium.SOLUTION: A process recipe adjusting method for an electronic device manufacturing system for obtaining metrology data associated with a deposition process includes: a processing logic including a combination of hardware and software or firmware receiving user input before identifying a process recipe; obtaining a thickness profile for the process recipe; obtaining an expected profile including a plurality of values indicative of a desired thickness for a plurality of layers of the process recipe; generating a correction profile based on the metrology data and the expected profile, the correction profile comprising a deposition time offset value for at least one layer of the plurality of layers; generating an updated process recipe by applying the correction profile to the process recipe; and causing a deposition step to be performed on a substrate according to the updated process recipe.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates to electrical components, and more particularly to methods and mechanisms for adjusting film deposition parameters during substrate manufacturing. [Background technology]

[0002] A product may be produced by performing one or more manufacturing processes using manufacturing equipment. For example, semiconductor manufacturing equipment may be used to fabricate semiconductor devices (e.g., substrates) through a semiconductor manufacturing process. The manufacturing equipment may deposit multiple layers of films on the surface of the substrate according to a process recipe and perform an etching process to form complex patterns in the deposited films. For example, the manufacturing equipment may perform a chemical vapor deposition (CVD) process to deposit other layers on the substrate. During this substrate manufacturing process, the thickness of each layer may vary due to continuously changing deposition parameters and changes in process chamber conditions (e.g., accumulation of contaminants, erosion on some components, etc.). Compensating for these variations is generally performed by manually increasing or decreasing the deposition time of subsequent layers to maintain the desired overall thickness of the film stack. However, such a process is prone to errors and may result in manufacturing delays and defective products. Therefore, a system capable of automatically adjusting film deposition parameters 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 present disclosure, an electronic device manufacturing system is capable of acquiring metrology data associated with a deposition process performed on a substrate according to a process recipe, the deposition process producing multiple layers on a surface of the substrate. The manufacturing system may further acquire an expected profile associated with the process recipe, the expected profile including multiple values ​​indicating desired thicknesses for the multiple layers of the process recipe. The manufacturing system may further generate a correction profile based on the metrology data and the expected profile, the correction profile including a deposition time offset value for at least one of the multiple layers. The manufacturing system may further generate an updated process recipe by applying the correction profile to the process recipe and causing a deposition step 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 explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram illustrating an exemplary system architecture, according to some embodiments. [Figure 2] 1 is a top schematic view of an exemplary manufacturing system, according to some embodiments. [Figure 3]1 is a cross-sectional schematic side view of an exemplary process chamber of an exemplary manufacturing system, in accordance with some embodiments. [Figure 4] 1 is a flowchart of a method for adjusting a process recipe based on a correction profile, according to some embodiments. [Figure 5] 1 is a flowchart of a method for determining a correction profile for a process recipe using a curve fitting method, in accordance with some embodiments. [Figure 6] 1 is a table illustrating exemplary correction profiles, according to some embodiments. [Figure 7A-B] 1 is a graph illustrating metrology data from a deposition process, according to an aspect of the present disclosure. [Figure 8] FIG. 1 is a block diagram illustrating a computer system according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0009] Described herein is technology directed to methods and mechanisms for adjusting film deposition parameters during substrate manufacturing. Films can be deposited on a surface of a substrate during a deposition process (e.g., a chemical vapor deposition (CVD) process, an atomic layer deposition (ALD) process, etc.) performed in a process chamber of a manufacturing system. For example, in a CVD process, a substrate is exposed to one or more precursors, which react on the substrate surface to produce a desired deposit. A film can include one or more layers of material formed during the deposition process, with each layer including a particular thickness gradient (e.g., a 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 complete and a final layer is formed for the film (referred to as the distal layer or distal end of the film). A film can include alternating layers of various materials. For example, a film may include alternating oxide and nitride layers (oxide-nitride-oxide-nitride stack or ONON stack), alternating oxide and polysilicon layers (oxide-polysilicon-oxide-polysilicon stack or OPOP stack), etc. Each set of alternating layers may be referred to as a loop. For example, a film may include 40 loops (e.g., 40 sets of oxide-nitride layers).

[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 material at the surface, which may then be removed by a vacuum system.

[0011] The process chamber 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 specific set of steps to be performed on the substrate during the process and can include one or more settings associated with each step. For example, a deposition process recipe can include a temperature setting for the process chamber, a pressure setting for the process chamber, flow rate settings for precursors for materials included in the film deposited on the substrate surface, etc. Thus, the thickness of each film layer is a function of these process chamber settings.

[0012] Typically, a process recipe includes a set of loops (e.g., 40 loops) where the layer thicknesses of some loops can differ from those of other loops. For example, a film stack can include 40 oxide-nitride loops (e.g., 80 layers, 40 layers of alternating oxide and 40 layers of nitride), where the first loop of the film stack has an oxide layer of a first thickness and a nitride layer of a second thickness, then the next 9 loops of the film stack have oxide layers of a third thickness and nitride layers of a fourth thickness, and the final 30 loops of the film stack have oxide layers of a fifth thickness and nitride layers of a sixth thickness.

[0013] During the substrate manufacturing process, the thickness of each loop may vary due to continuously changing deposition parameters and changes in process chamber conditions (e.g., contaminant accumulation, erosion of some components, etc.). Variations in layer thickness can cause the gas distribution plate to become closer or farther from the surface of the substrate, thus affecting plasma flow and / or temperature and causing further deformation to the film. In some manufacturing systems, these variations are compensated for by manually increasing or decreasing the deposition time of subsequent loops to maintain the desired overall thickness of the film stack. For example, if a first loop has a thickness greater than required by the process recipe, a technician can manually decrease the deposition time of a second loop in the process recipe to produce a thinner loop than required by the process recipe. This manual process of calculating the "step-time offset" for each loop and feeding the offset into a table is time-consuming and error-prone, and can result in manufacturing delays, loss of throughput, and / or film defects.

[0014] Aspects and embodiments of the present disclosure address these and other shortcomings of existing techniques by generating correction profiles for adjusting film deposition parameters during substrate manufacturing. In particular, a metrology instrument can generate metrology data for a substrate before, during, and / or after a manufacturing process (e.g., a deposition process) based on a process recipe. The metrology instrument 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 the substrate. The metrology instrument can generate the thickness profile at different times during the manufacturing process. For example, the metrology instrument can generate metrology data after deposition of each layer of a film stack, after deposition of each loop of a film stack, etc.

[0015] The manufacturing system can obtain an expected profile for a process recipe. The expected profile can include values ​​indicating a desired thickness of a film, a desired thickness of one or more layers of a film, and / or a desired thickness of one or more loops of a film. Using the thickness profile and the expected profile, the manufacturing system can generate a correction profile. The correction profile can include one or more adjustments or offsets (e.g., corrective actions) to be applied to parameters of a process recipe or a process chamber. For example, the correction profile can include adjusting a deposition time, adjusting a temperature setting for a process chamber, adjusting a pressure setting for a process chamber, adjusting a flow rate setting for a precursor, adjusting a power supplied to a process chamber, adjusting a ratio of two or more settings, etc., for a particular layer or loop of a process recipe. In some embodiments, the manufacturing system can generate the correction profile by using one or more equations or mathematical models. For example, processing logic can use data values ​​from the expected profile and / or the thickness profile to generate a curve fitting model, and then use the curve fitting model to determine one or more offset time values ​​for one or more steps of the current deposition process. An offset may be applied to each deposition step to adjust the layer thickness for that step so that the film thickness at the end of deposition is the same as the film thickness dictated by the process recipe.

[0016] In some embodiments, the processing logic can generate a correction profile using a machine learning model or using an inference engine. The manufacturing system can then use the correction profile to adjust process recipe parameters (e.g., deposition time) for one or more steps of the process recipe (e.g., for one or more layers or loops of the process recipe). This allows the manufacturing system to generate adjustments for specific process steps of the process recipe.

[0017] Aspects of the present disclosure yield technical advantages such as a significant reduction in the time required to perform parameter optimization of a process recipe. Aspects of the present disclosure yield further technical advantages such as a significant reduction in the time required to detect problems or failures experienced by substrates during the manufacturing process, as well as improved energy consumption. The present disclosure can also yield diagnostic data and corrective actions to avoid inconsistent and abnormal products and unplanned user time or downtime.

[0018] FIG. 1 illustrates an exemplary computer system architecture 100 according to aspects of the present 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, manufacturing equipment 124, metrology equipment 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 equipment 124 may include a sensor 126 configured to capture data about substrates being processed in the manufacturing system. In some embodiments, the manufacturing equipment 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 front-opening unified pod (FOUP) radio frequency identification (RFID) reader 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).

[0019] The manufacturing equipment 124 can perform operations according to a recipe or over a period of time to manufacture products, such as electronic devices. The manufacturing equipment 124 can include a process chamber, such as the process chamber 400 described with respect to FIG. 4. The manufacturing equipment 124 can perform processes on substrates (e.g., wafers) in the process chamber. Examples of substrate processes include deposition processes for depositing one or more layers of a film on the surface of the substrate, etching processes for forming a pattern on the surface of the substrate, etc. The manufacturing equipment 124 can perform each process according to a process recipe. The process recipe defines a specific set of steps to be performed on the substrate during the process and can include one or more settings associated with each step. For example, a deposition process recipe can include temperature settings for the process chamber, pressure settings for the process chamber, flow rate settings for precursors for materials included in the film deposited on the substrate surface, etc.

[0020] In some embodiments, the fabrication equipment 124 includes sensors 126 configured to generate data related to substrates processed in the fabrication system 100. For example, a process chamber may include one or more sensors configured to generate spectral or non-spectral data related to 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 the concentration of one or more materials deposited on the surface of the substrate. The sensors 126 configured to generate spectral data related to the substrate may include reflectometry sensors, ellipsometry sensors, thermal spectral sensors, capacitive sensors, etc. The sensors 126 configured to generate non-spectral data related to the substrate may include temperature sensors, pressure sensors, flow sensors, voltage sensors, etc. Further details regarding the fabrication equipment 124 are provided with respect to FIGS. 3 and 4.

[0021] In some embodiments, the sensors 126 provide sensor data (e.g., sensor values, features, trace data) related to the manufacturing equipment 124 (e.g., related to the manufacturing of a corresponding product, such as a wafer, by the manufacturing equipment 124). The manufacturing equipment 124 can manufacture products according to a recipe or by performing an operation over a period of time. Sensor data received over a period of time (e.g., corresponding to at least a portion of a recipe or operation) may be referred to as trace data (e.g., historical trace data, current trace data, etc.) received from different sensors 126 over time. The sensor data may 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 may be related to or indicative of manufacturing parameters, such as hardware parameters, such as settings or components (e.g., size, type, etc.) of the manufacturing equipment 124, or process parameters of the manufacturing equipment 124. The sensor data may be provided while the manufacturing equipment 124 is performing the manufacturing process (e.g., equipment readings as the product is being processed). The sensor data may be different for each substrate.

[0022] The metrology equipment 128 can provide metrology data related to substrates processed by the fabrication equipment 124. The metrology data can include values ​​of film property data (e.g., wafer-space 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 dimensions of one or more features contained on the surface of the substrate, critical dimension uniformity across the surface of the substrate, edge placement error, etc.). The metrology data can be of a finished or nearly finished product. The metrology data can be different for each substrate. The metrology data can be generated using, for example, reflectometry techniques, ellipsometry techniques, TEM techniques, etc.

[0023] In some embodiments, the metrology instrument 128 may be included as part of the fabrication equipment 124. For example, the metrology instrument 128 may be included within or coupled to a process chamber and configured to generate metrology data about 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 instances, the metrology instrument 128 may be referred to as an in-situ metrology instrument. In another example, the metrology instrument 128 may be coupled to another station of the fabrication equipment 124. For example, the metrology instrument may be coupled to a transfer chamber, such as the transfer chamber 310 in FIG. 3 , a load lock, such as the load lock 320, or a factory interface, such as the factory interface 306.

[0024] The client device 120 may include a computing device such as a personal computer (PC), a laptop, a mobile phone, a smartphone, 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) that allows a user to provide as input metrology measurements for substrates processed in a manufacturing system. The client device 120 may include a corrective action component 122. The corrective action component 122 may receive user input (e.g., via a graphical user interface (GUI) displayed via the client device 120) of instructions related to 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 instructions for corrective actions from the predictive system 110 and causes the corrective actions to be implemented. Each client device 120 may include an operating system that enables a user to one or more of generate, view, or edit data (e.g., instructions related to the manufacturing equipment 124, corrective actions related to the manufacturing equipment 124, etc.).

[0025] The data store 140 may be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. The data store 140 may include multiple storage components (e.g., multiple drives or multiple databases) that may span multiple computing devices (e.g., multiple server computers). The data store 140 may store data related to processing substrates on the manufacturing equipment 124. For example, the data store 140 may store data (referred to as process data) collected by sensors 126 on the manufacturing equipment 124 before, during, or after a substrate process. Process data may 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 may also store spectral or non-spectral data related to a portion of a substrate processed on the manufacturing equipment 124. The spectral data may include historical spectral data and / or current spectral data.

[0026] 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 a recipe name, a recipe step number, a preventative maintenance indicator, an 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 context data (e.g., context data associated with a current or future process to be performed on a previous substrate). The context data may further include and identify sensors associated with particular subsystems of a process chamber.

[0027] The data store 140 can also store task data. The task data can include one or more sets of steps to be performed on a substrate during a deposition process and can include one or more settings associated with each step. For example, task data for a deposition process can include temperature settings for a process chamber, pressure settings for the process chamber, flow rate settings for precursors for the material of the film to be deposited on the substrate, etc. In another example, the task data can include control pressures at defined pressure points for flow values. Task data can refer to historical 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).

[0028] In some embodiments, the data store 140 can store a predicted profile, a thickness profile, and a correction profile. The predicted profile can include one or more data points related to a desired film profile expected to be produced by a process recipe. In some embodiments, the predicted profile can include a desired thickness of the film, a desired thickness of one or more layers of the film, and / or a desired thickness of one or more loops of the film, etc. The thickness profile can include one or more data points related to a current film profile generated by the fabrication equipment 124. For example, the thickness profile can include a measured thickness of the film, a measured thickness of one or more layers of the film, and / or a measured thickness of one or more loops of the film, etc. The thickness profile can be measured using the metrology equipment 128. The correction profile can include one or more adjustments or offsets to be applied to parameters of the process chamber or process recipe. For example, the correction profile may include adjusting deposition time for a film layer and / or loop, adjusting temperature settings for a process chamber, adjusting pressure settings for a process chamber, adjusting flow rate settings for precursors for materials included in the film deposited on the substrate surface, adjusting power supplied to a process chamber, adjusting a ratio of two or more settings, etc. The correction profile may be generated by comparing an expected profile (e.g., a thickness profile expected to be produced by a process recipe) and determining adjustments to be applied to parameters of the process recipe to achieve the expected profile using algorithms, libraries of known fault patterns, etc. The correction profile may be applied to steps associated with a deposition process, an etch process, etc.

[0029] In some embodiments, data store 140 may 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 substrates 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 may be inaccessible by users of the manufacturing system. In other or similar embodiments, some portions of the data stored in data store 140 may be inaccessible by users, while other portions of the data stored in data store 140 may be accessible by users. In some embodiments, one or more portions of the data stored in data store 140 may be encrypted using an encryption mechanism unknown to the users (e.g., the data is encrypted using a private encryption key). In other or similar embodiments, data store 140 may include multiple data stores, where data that is inaccessible 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.

[0030] In some embodiments, the data store 140 may be configured to store data related to known failure patterns. A failure pattern may be one or more values ​​(e.g., a vector, a scalar, etc.) related to one or more problems or failures associated with a process chamber subsystem. In some embodiments, a failure pattern may be associated with a corrective action. For example, a failure pattern may include a parameter adjustment step to correct a problem or failure indicated by the failure pattern. For example, a predictive system may compare a determined failure pattern to a library of known failure patterns to determine the type of failure experienced by the subsystem, the cause of the failure, a recommended corrective action to correct the failure, etc.

[0031] 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-mounted server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, a graphics processing unit (GPU), an accelerator application-specific integrated circuit (ASIC) (e.g., a tensor processing unit (TPU)), etc.

[0032] Server machine 170 includes a training set generator 172 capable of generating training datasets (e.g., a set of data inputs and a set of target outputs) for training, validating, and / or testing machine learning model 190. Machine learning model 190 may be any algorithmic model capable of learning from data. In some embodiments, training set generator 172 may partition the training data into a training set, a validation set, and a test set. In some embodiments, prediction system 110 generates multiple sets of training data.

[0033] 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, a processing device, etc.), software (such as instructions running on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. The training engine 182 may be capable of training one or more machine learning models 190. A machine learning model 190 may refer to a model artifact 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 and provide a machine learning model 190 that captures these patterns, mapping the training inputs to target outputs (answers to be predicted). The machine learning model 190 may use one or more of statistical modeling, support vector machines (SVMs), radial basis functions (RBFs), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithms (k-NNs), linear regression, random forests, neural networks (e.g., artificial neural networks), and the like.

[0034] 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 recurrent layer that maps features to a desired output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters. In lower layers, pooling may be performed to address nonlinearities, and a multilayer perceptron is typically added above the lower layers 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. A deep neural network includes a hierarchy of layers, with different layers learning different representation levels corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into slightly more abstract and complex representations. In plasma process tuning, for example, the raw input may 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 may comprise feature data related to the status of one or more zones of a plasma processing system's control elements (e.g., zone orientation, plasma exposure duration, etc.), and a third layer may include a starting recipe (e.g., a recipe used as a starting point for determining an updated process recipe for processing a substrate to produce a process result that meets a threshold criterion). In particular, a deep learning process can independently learn which features should optimally be placed at which level. The "deep" in "deep learning" refers to the number of layers through which data is transformed. More precisely, a deep learning system has substantial credit assignment path (CAP) depth. A CAP is a chain of transformations from input to output.CAPs potentially represent causal connections between inputs and outputs. For feedforward neural networks, the depth of a CAP can be the depth of the network, which can be the number of hidden layers + 1. For recurrent neural networks, where a signal can propagate through layers more than once, the CAP depth is potentially unlimited.

[0035] In one embodiment, the one or more machine learning models are recurrent neural networks (RNNs). An RNN is a type of neural network that includes memory to enable the neural network to capture time dependencies. The RNN is capable of learning an input-output mapping that depends on both current and past inputs. The RNN will account for past and future flow measurements and make predictions based on this continuous measurement information. The RNN can be trained using a training data set to generate a fixed number of outputs (e.g., to determine a set of substrate processing rates, to determine modifications to a substrate process recipe). One type of RNN that can be used is a long short-term memory (LSTM) neural network.

[0036] Training a neural network can be accomplished in a supervised learning fashion, which involves feeding a training dataset consisting of labeled inputs through the network, observing its outputs, 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's weights across all its layers and nodes so that the error is minimized. In many applications, repeating this process across many labeled inputs in the training dataset results in a network that can produce correct outputs when presented with inputs that differ from those present in the training dataset.

[0037] A training data set containing hundreds, thousands, tens of thousands, hundreds of thousands, or more pieces of sensor data and / or process result data (e.g., metrology data, such as one or more thickness profiles associated with the sensor data) may be used to form the training data set.

[0038] To achieve training, the processing logic may input the training dataset(s) to one or more untrained machine learning models. Prior to inputting the first input to the machine learning model, the machine learning model may be initialized. The processing logic trains the untrained machine learning model(s) based on the training dataset(s) to generate one or more trained machine learning models that perform the various steps as described above. The training may be performed by inputting one or more of the sensor data to the machine learning model one by one.

[0039] A machine learning model processes inputs and generates outputs. An artificial neural network includes an input layer consisting of values ​​at data points. The next layer is called a hidden layer, and nodes in the hidden layer each receive one or more of the input values. Each node includes parameters (e.g., weights) to apply to the input values. Thus, each node essentially inputs the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to produce an output value. The next layer can be another hidden layer or an output layer. In either case, nodes in the next layer receive output values ​​from nodes in the previous layer, and each node applies weights to those values ​​and then generates its own output value. This can be performed in each layer. The final layer is the output layer, with one node for each class, prediction, and / or output that the machine learning model can produce.

[0040] Thus, the output may include one or more predictions or inferences. For example, the output prediction or inference may include one or more predictions of film buildup on a chamber component, erosion of a chamber component, predicted failure of a chamber component, etc. Processing logic determines an error (i.e., classification error) based on the difference between the output (e.g., prediction or inference) of the machine learning model and a target label associated with the input training data. Processing logic adjusts the weights of one or more nodes in the machine learning model based on the error. An error term or delta may be determined for each node in the artificial neural network. Based on this error, the artificial neural network adjusts one or more of its parameters (weights for one or more inputs of the node) for one or more of its nodes. Parameters may be updated in a backpropagation manner, such that nodes in the top layer are updated first, followed by nodes in the next layer, and so on. The artificial neural network includes multiple layers of "neurons," each layer receiving values ​​as inputs from neurons in the previous layer. The parameters for 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 for one or more neurons in one or more layers in the artificial neural network.

[0041] 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 in parameters relative to 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, training can be completed. Once the machine learning model is trained, a reserved portion of the training dataset can be used to test the model.

[0042] Once one or more trained machine learning models 190 are generated, they may be stored in the prediction server 112 as the prediction component 114 or as components of the prediction component 114.

[0043] The validation engine 184 may be capable of validating the machine learning model 190 using the corresponding set of validation set features from the training set generator 172. Once the model parameters are optimized, model validation may be performed to determine whether the model has 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 validation set features. The validation engine 184 may discard trained machine learning models 190 that have an accuracy that does not meet a threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting a 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 with the highest accuracy among the trained machine learning models 190.

[0044] The testing engine 186 may be able to test the trained machine learning model 190 using a corresponding set of test set features from the training set generator 172. For example, a first trained machine learning model 190 trained using a first set of training set features may be tested using a first set of test set features. The testing engine 186 may determine the trained machine learning model 190 with the highest accuracy of all of the trained machine learning models based on the test set.

[0045] As described in more detail below, the prediction server 112 includes a prediction component 114 that is capable of running a trained machine learning model 190 against current sensor data inputs to generate a correction profile using one or more equations and / or obtain one or more outputs indicative of correction or adjustment data (e.g., deposition time adjustment data for each layer and / or loop of a process recipe).

[0046] Client devices 120, manufacturing equipment 124, sensors 126, measurement equipment 128, prediction server 112, data store 140, server machine 170, and server machine 180 may be coupled to one another via network 130. In some embodiments, network 130 is a public network that provides client devices 120 with access to prediction server 112, data store 140, and other publicly available computing devices. In some embodiments, network 130 is a private network that provides client devices 120 with access to manufacturing equipment 124, measurement equipment 128, data store 140, and other privately available computing devices. Network 130 may 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.

[0047] It should be noted that in some other embodiments, the functionality of server machines 170 and 180 and prediction server 112 may be provided by fewer machines. For example, in some embodiments, server machines 170 and 180 may be combined into a single machine, and in some other or similar embodiments, server machines 170 and 180 and prediction server 112 may be combined into a single machine.

[0048] 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 or multiple components acting together.

[0049] In embodiments, 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 set of individual users federated as a group of administrators may be considered a "user."

[0050] In some embodiments, a manufacturing system may include two or more process chambers. For example, the exemplary manufacturing system 200 of FIG. 2 shows multiple process chambers 214, 216, and 218. Note that in some embodiments, the data acquired to train the machine learning model and the data collected to provide 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 provide 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 provide as input to the machine learning model may be associated with a process chamber of a second manufacturing system.

[0051] 2 is a top schematic view of an exemplary manufacturing system 200 according to an embodiment of the present disclosure. The manufacturing system 200 is capable of performing one or more processes on a substrate 202. The substrate 202 may be any suitable rigid, fixed-dimension, planar article suitable for fabricating electronic devices or circuit components thereon, such as, for example, a silicon-containing disk or wafer, a patterned wafer, a glass plate, or the like.

[0052] The manufacturing system 200 may include a process tool 204 and a factory interface 206 coupled to the process tool 204. The process tool 204 may include a housing 208 having a transfer chamber 210 therein. The transfer chamber 210 may include one or more process chambers 214, 216, 218 (also referred to as processing chambers) arranged around and coupled thereto. The process chambers 214, 216, 218 may be coupled to the transfer chamber 210 through respective ports, such as slit valves. The transfer chamber 210 may also include a transfer chamber robot 212 configured to transfer substrates 202 between the process chambers 214, 216, 218, load locks 220, etc. The transfer chamber robot 212 may include one or more arms, each including one or more end effectors at the end of each arm. The end effectors may be configured to handle specific objects, such as wafers, sensor disks, sensor tools, etc.

[0053] The process chambers 214, 216, 218 may be adapted to perform any number of processes on the substrate 202. The same or different substrate processes may occur in each processing chamber 214, 216, 218. Substrate processes may include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, etc. Other processes may be performed on the substrate therein. The process chambers 214, 216, 218 may each include one or more sensors configured to capture data about the substrate 202 before, after, or during substrate processing. For example, the one or more sensors may be configured to capture spectral and / or non-spectral data about a portion of the substrate 202 during substrate processing. In other or similar embodiments, the one or more sensors may be configured to capture data related to the environment within the process chambers 214, 216, 218 before, after, or during substrate processing. For example, one or more sensors may be configured to capture data related to the temperature, pressure, gas concentrations, etc. of the environment within the process chambers 214, 216, 218 during substrate processing.

[0054] A load lock 220 may also be coupled to the housing 208 and the transfer chamber 210. The load lock 220 may be configured to interface with and be coupled to the transfer chamber 210 on one side and the factory interface 206 on the other. The load lock 220, in some embodiments, may have an environmentally controlled atmosphere that may be changed from a vacuum environment (where substrates may be transferred to and from the transfer chamber 210) to an inert gas environment at or near atmospheric pressure (where substrates may be transferred to and from the factory interface 206). The factory interface 206 may be any suitable enclosure, such as, for example, a front-end equipment module (EFEM). The factory interface 206 may be configured to receive substrates 202 from substrate carriers 222 (e.g., front-opening unified pods (FOUPs)) docked at various load ports 224 of the factory interface 206. A factory interface robot 226 (shown in dotted lines) may be configured to transfer substrates 202 between carriers 222 (also called containers) and load locks 220. Carriers 222 may be substrate storage carriers or replacement part storage carriers.

[0055] Fabrication system 200 may also be connected to a client device (not shown) configured to provide information about fabrication system 200 to a user (e.g., an operator). In some embodiments, the client device may provide information to a user of fabrication system 200 via one or more graphical user interfaces (GUIs). For example, the client device may provide information via the GUI regarding a target thickness profile for a film to be deposited on the surface of substrate 202 during a deposition process performed in process chambers 214, 216, 218. The client device may also provide information regarding modifications to a process recipe in view of respective sets of deposition settings that are predicted to correspond to the target profile, according to embodiments described herein.

[0056] The manufacturing system 200 can also include a system controller 228. The system controller 228 can be and / or include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, etc. The system controller 228 can include one or more processing devices, which can be general-purpose processing devices such as a microprocessor, a central processing unit, etc. More specifically, the processing device can 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 processor implementing a combination of instruction sets. The processing device can 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 228 can 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 228 can execute instructions to perform any one or more of the methodologies and / or embodiments described herein. In some embodiments, the system controller 228 can execute instructions to perform one or more operations in the manufacturing system 200 according to a process recipe. The instructions can be stored in a computer-readable storage medium, which can include a main memory, a static memory, a secondary storage, and / or a processing device (during execution of the instructions).

[0057] The system controller 228 may receive data from sensors contained on or within various portions of the manufacturing system 200 (e.g., processing chambers 214, 216, 218, transfer chamber 210, load lock 220, etc.). In some embodiments, the data received by the system controller 228 may include spectral and / or non-spectral data about a portion of the substrate 202. In other or similar embodiments, the data received by the system controller 228 may include data related to processing the substrate 202 in the processing chambers 214, 216, 218, as previously described. The system controller 228 is described herein as receiving data from sensors contained within the process chambers 214, 216, 218. However, the system controller 228 may receive data from any portion of the manufacturing system 200 and use the data received from that portion, according to embodiments described herein. In an illustrative example, the system controller 228 may receive data from one or more sensors for the process chambers 214, 216, 218 before, after, or during substrate processing in the process chambers 214, 216, 218. The data received from the sensors in various portions of the manufacturing system 200 may be stored in a data store 250. The data store 250 may be included as a component within the system controller 228 or may be a separate component from the system controller 228. In some embodiments, the data store 250 may be the data store 140, described with respect to FIG. 1 .

[0058] FIG. 3 is a cross-sectional schematic side view of a process chamber 300 according to an embodiment of the present disclosure. In some embodiments, the process chamber 300 can correspond to the process chambers 214, 216, and 218 described with respect to FIG. 2 . The process chamber 300 can be used for a process in which a corrosive plasma environment is provided. For example, the process chamber 300 can be a chamber for a plasma etcher or a plasma etching reactor. In another example, the process chamber can be a chamber for a deposition process, as previously described. In one embodiment, the process chamber 300 includes a chamber body 302 and a showerhead 330 enclosing an interior volume 306. The showerhead 330 can include a showerhead base and a showerhead gas distribution plate. Alternatively, the showerhead 330 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 302 can be fabricated from other suitable materials, such as aluminum, stainless steel, or titanium (Ti). The chamber body 302 generally includes a sidewall 308 and a bottom 310. An exhaust port 326 may be defined in the chamber body 302 and may couple the interior volume 306 to a pumping system 328. The pumping system 328 may include one or more pumps and a throttle valve utilized to evacuate and adjust the pressure of the interior volume 306 of the process chamber 300.

[0059] The showerhead 330 may be supported on a sidewall 308 of the chamber body 302. The showerhead 330 (or lid) may be opened to allow access to the interior volume 306 of the process chamber 300 and may provide a seal to the process chamber 300 when closed. A gas panel 358 may be coupled to the process chamber 300 to provide process and / or cleaning gases to the interior volume 306 through the showerhead 330 or the lid and nozzles (e.g., through apertures in the showerhead or lid and nozzles). For example, the gas panel 358 may provide precursors for the material of the film 351 to be deposited on the surface of the substrate 302. In some embodiments, the precursors may include a silicon-based precursor or a boron-based precursor. The showerhead 330 may include a gas distribution plate (GDP) having multiple gas delivery holes 332 (also called channels) throughout the GDP. A substrate support assembly 348 is disposed in the interior volume 306 of the process chamber 300 below the showerhead 330. The substrate support assembly 348 uses, for example, an electrostatic chuck 350 to hold the substrate 302 during processing (eg, during a deposition process).

[0060] In some embodiments, the processing chamber 300 may include a metrology instrument (not shown) configured to generate in situ metrology measurements during a process performed in the process chamber 300. The metrology instrument may be operably coupled to a system controller (e.g., the previously described system controller 328). In some embodiments, the metrology instrument may be configured to generate metrology measurements (e.g., thickness) for the film 351 during a particular instance of the deposition process. The system controller may generate a thickness profile for the film 351 based on the metrology measurements received from the metrology instrument. In other or similar embodiments, the processing chamber 300 does not include a metrology instrument. In such embodiments, the system controller may receive one or more metrology measurements for the film 351 after completion of the deposition process in the process chamber 300. The system controller may determine a deposition rate based on the one or more metrology measurements and may generate a thickness profile for the film 351 based on the determined thickness gradient and the determined deposition rate of the deposition process.

[0061] 4 is a flowchart of a method 400 for adjusting a process recipe based on a correction profile according to an aspect of the present disclosure. Method 400 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as that running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 400 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more steps of method 400 may be performed by one or more other machines not shown in the figure. In some aspects, one or more steps of method 400 may be performed by manufacturing equipment 124 and / or client device 122.

[0062] In step 410, processing logic identifies a process recipe. In some embodiments, processing logic may receive user input identifying a process recipe. In other embodiments, processing logic may automatically select a process recipe. For example, processing logic may identify a process recipe for which at least one process run has been completed and a thickness profile has been generated. In yet another embodiment, processing logic may identify a process recipe based on a currently running process recipe. For example, processing logic may perform a deposition process on a substrate according to the 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 deposition time for each layer and / or loop of the process recipe, a temperature setting for the process chamber, a pressure setting for the process chamber, flow rates for precursors for materials included in the film 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 oxide and polysilicon layers, etc.

[0063] In step 412, processing logic obtains a thickness profile for the process recipe. The thickness profile includes one or more data values ​​associated with the film produced by the manufacturing equipment 124. For example, the thickness profile may include a measured thickness of the film, a measured thickness of one or more layers of the film, and / or a measured thickness of one or more loops of the film, etc. The thickness profile may be measured using metrology equipment 128. In some embodiments, the thickness profile is retrieved from data store 140.

[0064] In step 414, processing logic obtains an expected profile for the process recipe. The expected profile may include a desired thickness of the film, a desired thickness of one or more layers of the film, and / or a desired thickness of one or more loops of the film, etc. In some embodiments, the expected profile is retrieved from data store 140.

[0065] In step 416, processing logic generates a correction profile based on the expected profile and the thickness profile. The correction profile may include one or more correction actions to be applied to process recipe or process chamber parameters during one or more steps of the process recipe. In particular, the correction profile may include adjusting the deposition time of one or more layers and / or each of one or more loops, adjusting the temperature setting for the process chamber, adjusting the pressure setting for the process chamber, adjusting flow rate settings for precursors for materials included in the film deposited on the substrate surface, adjusting the power supplied to the process chamber, adjusting the ratio of two or more settings, etc. For example, the correction profile may include a deposition time adjustment for a loop of the process recipe. In some embodiments, the correction profile may include a set of parameter adjustments for each layer and / or loop of the process recipe. For example, the correction profile may include a deposition time adjustment for a first loop, a deposition time adjustment for a second loop, a deposition time adjustment for a third loop, and so on until the final loop. Each adjustment may be applied to a respective deposition step to adjust the thickness of one or more loops or layers, so that the film stack thickness is the same as the expected film thickness indicated by the expected profile. For example, if the expected film thickness after loop 39 is a first value (e.g., 20,000 nm), the expected film thickness after loop 40 is a second value (e.g., 20,500 nm), and the actual film thickness during the deposition run and after loop 39 is a third value (e.g., 20,050), the adjustment profile can indicate a correction to the deposition time for loop 40 (e.g., decreasing the deposition time for loop 40 by a certain period of time) so that the actual film thickness after loop 40 is equal to the expected film thickness (e.g., 20,500 nm).

[0066] In some embodiments, processing logic can generate the correction profile using one or more equations or mathematical models. For example, processing logic can use data values ​​from the expected profile and / or thickness profile to generate a curve fitting model, and then use the curve fitting model to determine offset time values ​​for a particular step in the current deposition process. This aspect of the disclosure is described in more detail in FIG. 5. In some embodiments, processing logic can generate the correction profile using a machine learning model (e.g., machine learning model 190) or using an inference engine.

[0067] In step 418, 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 the current deposition process. FIG. 6 is a table 600 illustrating example correction profiles. As shown, column 610 may include an index of a loop in the deposition process, and column 620 may include a time offset value to be applied to the deposition time of each corresponding loop.

[0068] In step 420, processing logic executes the deposition steps of the identified process recipe on the substrate according to the updated process recipe. For example, processing logic may deposit a first set of film layers (or loops) on the substrate, determine a thickness profile of the deposited film, generate a correction profile to correct for faults 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 (or loops) on the substrate. Thus, the deposition process recipe may be adjusted in real time or near real time. This process may be repeated for each deposition step of the process recipe.

[0069] FIG. 5 is a flowchart of a method 500 for determining a correction profile for a process recipe using a curve fitting method, according to an aspect of the present disclosure. Method 500 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as that running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 500 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more steps of method 500 may be performed by one or more other machines not shown in the figure. In some aspects, one or more steps of method 500 may be performed by manufacturing equipment 124 and / or client device 122. While FIG. 5 describes determining a correction profile using a curve fitting method, those skilled in the art will understand that other methods, formulas, and models, including, but not limited to, regression analysis, least squares methods, etc., may be used to generate the correction profile.

[0070] In step 512, processing logic obtains a thickness profile for the process recipe. The thickness profile includes one or more thickness data values ​​associated with the film produced by the manufacturing equipment 124. For example, the thickness profile may include a measured thickness of the film, a measured thickness of one or more layers of the film, and / or a measured thickness of one or more loops of the film, etc. The thickness profile may be measured using metrology equipment 128. In some embodiments, the thickness profile is retrieved from data store 140.

[0071] In step 514, processing logic obtains an expected profile for the process recipe. The expected profile may include a desired thickness of the film, a desired thickness of one or more layers of the film, and / or a desired thickness of one or more loops of the film, etc. In some embodiments, the expected profile is retrieved from data store 140.

[0072] In step 516, processing logic generates a polynomial. For example, a cubic polynomial is generated as follows: y=ax 3 +bx 2 +cx+d, where (x, y) are coordinates and a, b, c, and d are constants. A third-order polynomial is used as an example, and polynomials of any order can be used. In some embodiments, the polynomial is generated using a set of expected chamber wall residual thickness values ​​(e.g., chamber wall residual thickness after loop 1, chamber wall residual thickness after loop 2, chamber wall residual thickness after loop 3, etc.) as the x variable and a time value (e.g., expected deposition thickness at different chamber wall residual thicknesses) as the y coordinate. The (x, y) values ​​can be retrieved from the expected profile. Using the set of (x, y) coordinates, constants for the polynomial are determined. In some embodiments, the chamber wall residual thickness is the seasoning thickness plus the deposition thickness, where the deposition thickness is equal to the total thickness of all previous loops. The seasoning thickness can include a layer (e.g., a silicon oxide layer) on the chamber walls before a substrate is introduced into the chamber for processing. The deposited seasoning layer reduces the likelihood that contaminants will interfere with subsequent processing steps.

[0073] In step 518, processing logic determines a (y) value for a particular loop using the polynomial and the thickness profile. In particular, processing logic may receive input (e.g., user-based input, automatic input, etc.) indicating a layer or loop of the deposition process. Processing logic can then input the actual thickness for the loop or layer (obtained from the thickness profile) into the polynomial to calculate the (y) for that loop or layer.

[0074] At step 520, processing logic generates a correction value based on the (y) value. In one embodiment, the correction value is calculated using the following formula: Correction Value = (y [first loop] / y [current loop number])*t step where t stepis the expected time for the selected loop.

[0075] In step 522, processing logic enters the correction values ​​into a correction profile. The steps of method 500 may be performed for one or more of the remaining loops or layers of the process recipe.

[0076] 7A-7B are graphs illustrating metrology data from deposition processes using the same recipe, according to embodiments of the present disclosure. In particular, FIG. 7A is a graph illustrating thickness values ​​(represented along the y-axis) of a set of loops (represented along the x-axis) for four different locations along a substrate (locations 710-716). The thickness values ​​of each loop shown in FIG. 7A are not adjusted according to a correction profile. As can be seen, the thickness of each loop increases gradually. FIG. 7B is a graph illustrating the thickness values ​​of each loop adjusted according to a correction profile. As can be seen, the thickness of each loop remains relatively the same throughout the deposition process and does not increase gradually.

[0077] 8 is a block diagram illustrating a computer system 800, according to some 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 as a server computer 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 cellular phone, a web appliance, a server, a network router, 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. Furthermore, the term “computer” is intended to include any collection of computers that individually or together execute a set (or sets) of instructions to perform any one or more of the methodologies described herein.

[0078] In a further aspect, the computer system 800 may include a processing device 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.

[0079] The processing device 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 microprocessor implementing a combination of types of instruction sets), 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).

[0080] Computer system 800 may further include a network interface device 822 (e.g., coupled to network 874). Computer system 800 may also include a video display unit 810 (e.g., LCD), an alphanumeric input device 812 (e.g., keyboard), a cursor control device 814 (e.g., mouse), and a signal generating device 820.

[0081] In some embodiments, the data storage device 818 may include a non-transitory computer-readable storage medium 824 that may store instructions 826 encoding any one or more of the methods or functions described herein, including instructions encoding the components of FIG. 1 (e.g., the corrective action component 122, the prediction component 114, etc.) and for implementing the methods described herein.

[0082] The instructions 826 may also reside, completely or partially, within the volatile memory 804 and / or within the processing device 802 during execution thereof by the computer system 800; thus, the volatile memory 804 and the processing device 802 may also constitute machine-readable storage media.

[0083] Although 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 that is capable of storing or encoding a set of instructions for execution by a computer, causing the computer to perform any one or more of the methodologies 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.

[0084] The methods, components, and functions described herein may be implemented by discrete hardware components or may be integrated into the functionality of other hardware components, such as an ASIC, FPGA, DSP, or similar device. Furthermore, the methods, components, and functions may be implemented by firmware modules or functional circuits within a hardware device. Furthermore, the methods, components, and functions may be implemented in any combination of hardware devices and computer program components, or implemented in a computer program.

[0085] Unless otherwise specified, terms such as "receiving," "executing," "providing," "obtaining," "causing," "accessing," "determining," "adding," "using," "training," and the like refer to actions and processes performed or implemented by a computer system that manipulate data represented as physical (electronic) quantities in computer system registers and memory and transform that data into other data similarly represented as physical quantities in the computer system memory or registers, or other such information storage, transmission, or display device. Also, as used herein, terms such as "first," "second," "third," "fourth," and the like are meant as labels to distinguish between different elements and may not have any ordering meaning due to their numerical designation.

[0086] The examples described herein also relate to apparatus for performing the methods described herein. The apparatus may be specially constructed to perform the methods described herein, or the apparatus may comprise a general-purpose computer system that is selectively programmed by a computer program stored on the computer system. Such a computer program may be stored on a computer-readable tangible storage medium.

[0087] The methods and illustrative examples 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 steps. Example structures for a variety of these systems are set forth in the description above.

[0088] The above description is illustrative, and not limiting. While the present disclosure has been described with reference to particular illustrative 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 related to a deposition process performed on a substrate according to a process recipe, the deposition process producing a plurality of layers on a surface of the substrate; obtaining an expected profile associated with the process recipe, the expected profile including a plurality of values ​​indicative of desired thicknesses for a plurality of layers of the process recipe; generating a correction profile based on the metrology data and the expected profile, the correction profile including a deposition time offset value for at least one layer of the plurality of layers; generating an updated process recipe by applying the correction profile to the process recipe; causing a deposition step to be performed on the substrate according to the updated process recipe; A method comprising:

2. The method of claim 1 , wherein the metrology data indicates an actual thickness of one or more deposition layers.

3. The method of claim 1 , wherein the correction profile further comprises one or more correction actions to be applied to one or more setting parameters of the process recipe.

4. generating the correction profile, generating a polynomial based on at least one of the measurement data or the expected profile; determining one or more deposition time offset values ​​using the polynomial; and The method of claim 1 , comprising:

5. generating the correction profile includes: inputting the measurement data into a trained machine learning model; obtaining output values ​​of the trained machine learning model, the output values ​​being indicative of the correction profile; The method of claim 1 , comprising:

6. The method of claim 1 , wherein the correction profile includes deposition time offset values ​​for pairs of layers comprising different materials.

7. 10. The method of claim 1, wherein the deposition time offset value causes an actual film stack thickness produced according to the process recipe to match an expected film stack thickness produced according to the process recipe.

8. a memory device; a processing device operably coupled to the memory device to perform steps; An electronic device manufacturing system comprising: acquiring metrology data related to a deposition process performed on a substrate according to a process recipe, the deposition process producing a plurality of layers on a surface of the substrate; obtaining an expected profile associated with the process recipe, the expected profile including a plurality of values ​​indicative of desired thicknesses for a plurality of layers of the process recipe; generating a correction profile based on the metrology data and the expected profile, the correction profile including a deposition time offset value for at least one layer of the plurality of layers; generating an updated process recipe by applying the correction profile to the process recipe; causing a deposition step to be performed on the substrate according to the updated process recipe; An electronic device manufacturing system comprising:

9. The electronic device manufacturing system of claim 8 , wherein the metrology data indicates an actual thickness of one or more deposited layers.

10. 10. The electronic device manufacturing system of claim 8, wherein the correction profile further comprises one or more correction actions to be applied to one or more setting parameters of the process recipe.

11. Generating the correction profile comprises: generating a polynomial based on at least one of the measurement data or the expected profile; determining one or more deposition time offset values ​​using the polynomial; and 10. The electronic device manufacturing system of claim 8, comprising performing a process comprising:

12. Generating the correction profile comprises: inputting the measurement data into a trained machine learning model; obtaining output values ​​of the trained machine learning model, the output values ​​being indicative of the correction profile; 10. The electronic device manufacturing system of claim 8, comprising performing a process comprising:

13. The electronic device manufacturing system of claim 8 , wherein the correction profile includes deposition time offset values ​​for pairs of layers comprising different materials.

14. 10. The electronic device manufacturing system of claim 8, wherein the deposition time offset value causes an actual film stack thickness produced according to the process recipe to match an expected film stack thickness produced according to the process recipe.

15. A non-transitory computer-readable storage medium comprising instructions that, when executed by a processing device operably coupled to a memory, perform steps comprising: acquiring metrology data related to a deposition process performed on a substrate according to a process recipe, the deposition process producing a plurality of layers on a surface of the substrate; obtaining an expected profile associated with the process recipe, the expected profile including a plurality of values ​​indicative of desired thicknesses for a plurality of layers of the process recipe; generating a correction profile based on the metrology data and the expected profile, the correction profile including a deposition time offset value for at least one layer of the plurality of layers; generating an updated process recipe by applying the correction profile to the process recipe; causing a deposition step to be performed on the substrate according to the updated process recipe; 1. A non-transitory computer-readable storage medium comprising:

16. 16. The non-transitory computer-readable storage medium of claim 15, wherein the metrology data indicates an actual thickness of one or more deposition layers.

17. 16. The non-transitory computer-readable storage medium of claim 15, wherein the correction profile further comprises one or more correction actions to be applied to one or more setting parameters of the process recipe.

18. Generating the correction profile comprises: generating a polynomial based on at least one of the measurement data or the expected profile; determining one or more deposition time offset values ​​using the polynomial; and 20. The non-transitory computer-readable storage medium of claim 17, comprising performing steps including:

19. Generating the correction profile comprises: inputting the measurement data into a trained machine learning model; obtaining output values ​​of the trained machine learning model, the output values ​​being indicative of the correction profile; 20. The non-transitory computer-readable storage medium of claim 17, comprising performing steps including:

20. 20. The non-transitory computer-readable storage medium of claim 17, wherein the correction profile includes deposition time offset values ​​for pairs of layers comprising different materials.

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