Method and mechanism for adjusting film deposition parameters during substrate manufacturing

Abstract

An electronic device manufacturing system capable of acquiring measurement data related to a deposition process executed on a substrate according to a process recipe, wherein the deposition process generates a plurality of layers on the surface of the substrate. The manufacturing system can further acquire an expected profile related to the process recipe, the expected profile including a plurality of values indicating desired thicknesses for a plurality of layers of the process recipe. The manufacturing system can further generate a correction profile based on the measurement data and the expected profile, the correction profile including deposition time offset values for at least one of the plurality of layers. The manufacturing system can further generate an updated process recipe by applying the correction profile to the process recipe and cause a deposition step to be executed on the substrate according to the updated process recipe.

Description

Technical Field

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

Background Art

[0002] Products can be fabricated by performing one or more manufacturing processes using manufacturing equipment. For example, semiconductor manufacturing equipment can be used to fabricate semiconductor devices (e.g., substrates) via semiconductor manufacturing processes. The manufacturing equipment can deposit multiple layers of a film on the surface of a substrate according to a process recipe and can perform an etching process to form complex patterns in the deposited film. For example, the manufacturing equipment can perform a chemical vapor deposition (CVD) process to deposit other layers on the substrate. During this substrate manufacturing process, due to continuously changing deposition parameters and changes in the state of the process chamber (e.g., accumulation of contaminants, erosion of some components, etc.), the thickness of each layer may vary. 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 processes are prone to errors and can 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 present disclosure to provide a basic understanding of some aspects of the present disclosure. This summary is not an extensive overview of the present disclosure. This summary neither identifies the main or important elements of the present disclosure nor delimits the scope of specific embodiments of the present disclosure or the scope of the claims. Its sole purpose is to present some concepts of the present disclosure in a simplified form as a prelude to the more detailed description presented later.

[0004] In one aspect of the present disclosure, an electronic device manufacturing system is to obtain measurement data related to a deposition process executed on a substrate according to a process recipe, wherein the deposition process can generate a plurality of layers on the surface of the substrate, and it is possible to obtain the measurement data. The manufacturing system is further to obtain an expected profile related to the process recipe, wherein the expected profile includes a plurality of values indicating desired thicknesses for a plurality of layers of the process recipe. The manufacturing system is further to generate a correction profile based on the measurement data and the expected profile, wherein the correction profile includes deposition time offset values for at least one of the plurality of layers. The manufacturing system is further to generate an updated process recipe by applying the correction profile to the process recipe and cause a deposition step to be executed on the substrate according to the updated process recipe.

[0005] A further aspect of the present disclosure includes a method 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 shown, by way of example and not limitation, in the figures of the accompanying drawings.

Brief Description of the Drawings

[0008]

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DETAILED DESCRIPTION OF THE INVENTION

[0009] This specification describes techniques directed to methods and mechanisms for adjusting film deposition parameters during substrate manufacturing. The film can be deposited on the surface of a substrate during a deposition process (e.g., a chemical vapor deposition (CVD) process, an atomic layer deposition (ALD) process, etc.) that is executed in a process chamber of a manufacturing system. For example, in a CVD process, the substrate is exposed to one or more precursors, and the one or more precursors react on the substrate surface to produce a desired deposit. The film can include one or more layers of material that are formed during the deposition process, and each layer can include a specific thickness gradient (e.g., a change in thickness along the 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 can 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). The film can include alternating layers of various materials. For example, the film can include alternating layers of oxide and nitride (an oxide-nitride-oxide-nitride stack or ONON stack), alternating oxide layers and polysilicon layers (an oxide-polysilicon-oxide-polysilicon stack or OPOP stack), etc. Each set of alternating layers can sometimes be referred to as a loop. For example, the film can include 40 loops (e.g., 40 sets of oxide-nitride layers).

[0010] The film can undergo, 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 fabricate the finished substrate. The etching process can include exposing the sample surface to a high-energy process gas (e.g., plasma) to decompose the material on that surface, which can then be removed by a vacuum system.

[0011] The process chamber can execute each substrate manufacturing process (e.g., deposition process, etching process, polishing process, etc.) according to a process recipe. The process recipe can define 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, a flow rate setting for the precursor for the material contained in the film deposited on the substrate surface, etc. Therefore, the thickness of each film layer is correlated with these process chamber settings.

[0012] Generally, a process recipe can include a set of loops (e.g., 40 loops) where the thickness of some loops of layers can be different from those of other loops. For example, a film stack can include 40 oxide-nitride loops (e.g., 80 layers, 40 layers of alternating oxides and 40 layers of nitrides), 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 an oxide layer of a third thickness and a nitride layer of a fourth thickness, and the final 30 loops of the film stack have an oxide layer of a fifth thickness and a nitride layer of a sixth thickness.

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

[0014] Aspects and embodiments of the present disclosure address these and other shortcomings of the existing technology by generating a correction profile to adjust the film deposition parameters during substrate manufacturing. In particular, the measurement device can generate measurement data for the substrate before, during, and / or after the manufacturing process (e.g., deposition process) based on the process recipe. The measurement device can use the measurement data to generate a thickness profile indicating one or more thickness values across the surface of the substrate. The thickness profile can indicate the thickness of the film on the substrate. The measurement device can generate the thickness profile at different times during the manufacturing process. For example, the measurement device can generate measurement data after the deposition of each layer of the film stack, after the deposition of each loop of the 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 the film, desired thicknesses of one or more layers of the film, and / or desired thicknesses of one or more loops of the 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., correction actions) to be applied to the parameters of the process recipe or the process chamber. For example, the correction profile can include adjustments to the deposition time for a particular layer or loop of the process recipe, adjustments to the temperature setting for the process chamber, adjustments to the pressure setting for the process chamber, adjustments to the flow rate setting for the precursor, adjustments to the power supplied to the process chamber, adjustments to the ratio of two or more settings, etc. In some embodiments, the manufacturing system can generate the correction profile by using one or more equations or mathematical models. For example, the 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 can be applied to each deposition step to adjust the layer thickness such that the film thickness at the end of deposition is the same as the film thickness indicated by the process recipe.

[0016] In some embodiments, the processing logic can generate the correction profile by using a machine learning model or by using an inference engine. The manufacturing system can then use the correction profile to adjust the 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 enables the manufacturing system to generate adjustments for a particular process step of the process recipe.

[0017] Aspects of the present disclosure result in a significant reduction in the time required to perform optimization of process recipe parameters, which is a technical advantage. Aspects of the present disclosure further result in a significant reduction in the time required to detect problems or faults that a substrate undergoes during a manufacturing process, as well as technical advantages such as improvement in energy consumption. The present disclosure can also result in generating diagnostic data and performing corrective actions to avoid inconsistent and abnormal products and unexpected user time or downtime.

[0018] FIG. 1 shows an exemplary computer system architecture 100 according to an aspect of the present disclosure. In some embodiments, the computer system architecture 100 can 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, measurement 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 can be part of a prediction system 110. The prediction system 110 can further include server machines 170 and 180. The manufacturing equipment 124 can include a sensor 126 configured to capture data about the substrate being processed in the manufacturing system. In some embodiments, the manufacturing equipment 124 and the sensor 126 can be part of a sensor system that includes a sensor server (e.g., a field service server (FSS) in a manufacturing facility) and a sensor identifier reader (e.g., a front opening unified pod (FOUP) radio frequency identification (RFID) reader for a sensor system). In some embodiments, the measurement equipment 128 can be part of a measurement system that includes a measurement server (e.g., a measurement database, a measurement folder, etc.) and a measurement identifier reader (e.g., a FOUP RFID reader for a measurement system).

[0019] The manufacturing machine 124 can manufacture products, such as electronic devices, by executing operations according to a recipe or over a certain period of time. The manufacturing machine 124 can include a process chamber, such as the process chamber 400 described with respect to FIG. 4. The manufacturing machine 124 can perform a process on a substrate (e.g., a wafer, etc.) in the process chamber. Examples of substrate processes include a deposition process for depositing one or more layers of a film on the surface of the substrate, an etching process for forming a pattern on the surface of the substrate, and the like. The manufacturing machine 124 can execute each process according to a process recipe. The process recipe can define 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, a flow rate setting for a precursor for a material included in the film deposited on the substrate surface, and the like.

[0020] In some embodiments, the manufacturing machine 124 includes a sensor 126 configured to generate data related to a substrate being processed in the manufacturing system 100. For example, the process chamber can include one or more sensors configured to generate spectral or non-spectral data related to the 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 sensor 126 can indicate the concentration of one or more materials deposited on the surface of the substrate. The sensor 126 configured to generate spectral data related to the substrate can include a reflectance measurement sensor, an ellipsometry sensor, a thermal spectrum sensor, a capacitive sensor, and the like. The sensor 126 configured to generate non-spectral data related to the substrate can include a temperature sensor, a pressure sensor, a flow rate sensor, a voltage sensor, and the like. Further details regarding the manufacturing machine 124 are provided with respect to FIGS. 3 and 4.

[0021] In some embodiments, sensor 126 provides sensor data (e.g., sensor values, features, trace data) related to manufacturing equipment 124 (e.g., related to manufacturing a corresponding product such as a wafer by manufacturing equipment 124). Manufacturing equipment 124 can manufacture a product by operating according to a recipe or over a certain period of time. Sensor data received over a certain period of time (e.g., corresponding to at least a part of the 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 can include one or more values such as temperature (e.g., heater temperature), interval (SP), pressure, high frequency radio frequency (HFRF), voltage of an electrostatic chuck (ESC), current, material flow, power, voltage, etc. The sensor data can be related to or indicative of manufacturing parameters, such as hardware parameters (e.g., settings or components of manufacturing equipment 124, such as size, type, etc.) or process parameters of manufacturing equipment 124. The sensor data can be provided while manufacturing equipment 124 is executing a manufacturing process (e.g., read values of the equipment while processing a product). The sensor data can be different for each substrate.

[0022] The measuring device 128 can provide measurement data related to the substrate processed by the manufacturing device 124. The measurement data can include values such as 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 measurement data can further include values of one or more surface profile property data (e.g., etching rate, uniformity of etching rate, critical dimensions of one or more features included on the surface of the substrate, uniformity of critical dimensions across the surface of the substrate, edge placement error, etc.). The measurement data can be of a completed or nearly completed product. The measurement data can be different for each substrate. The measurement data can be generated using, for example, reflectance measurement techniques, ellipsometry techniques (polarization analysis techniques), TEM techniques, etc.

[0023] In some embodiments, the measuring device 128 can be included as part of the manufacturing device 124. For example, the measuring device 128 can be included inside or coupled to the process chamber and configured to generate measurement data about the substrate before, during, and / or after a process (e.g., deposition process, etching process, etc.) while the substrate remains in the process chamber. In some cases, the measuring device 128 may be referred to as an in-situ measuring device. In another example, the measuring device 128 can be coupled to another station of the manufacturing device 124. For example, the measuring device can 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 can include computing devices such as a personal computer (PC), laptop, mobile phone, smartphone, tablet computer, netbook computer, network-connected television (“smart TV”), network-connected media player (e.g., Blu-ray player), set-top box, over-the-top (OTT) streaming device, operator box, etc. In some embodiments, measurement data can be received from the client device 120. The client device 120 can display a graphical user interface (GUI), and the GUI enables the user to provide, as input, measurement values of a substrate processed in the manufacturing system. The client device 120 can include a correction action component 122. The correction action component 122 can receive user input related to an instruction for the manufacturing equipment 124 (e.g., via a graphical user interface (GUI) displayed via the client device 120). In some embodiments, the correction action component 122 sends an instruction to the prediction system 110, receives an output (e.g., prediction data) from the prediction system 110, determines a correction action based on the output, and causes the correction action to be implemented. In some embodiments, the correction action component 122 receives an instruction for a correction action from the prediction system 110 and causes the correction action to be implemented. Each client device 120 can include an operating system that enables the user to perform one or more of generating, viewing, or editing data (e.g., instructions related to the manufacturing equipment 124, correction actions related to the manufacturing equipment 124, etc.).

[0025] 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 related to processing substrates in the manufacturing equipment 124. For example, the data store 140 can store data (referred to as process data) collected by the sensor 126 in the manufacturing equipment 124 before, during, or after the substrate process. The process data can refer to historical process data (e.g., process data generated for previous substrates processed in the manufacturing system) and / or current process data (e.g., process data generated for the current substrate being processed in the manufacturing system). The data store can also store spectral or non-spectral data related to a portion of the substrate being processed in the manufacturing equipment 124. The spectral data can include historical spectral data and / or current spectral data.

[0026] The data store 140 can also store context data related to one or more substrates being processed in the manufacturing system. The context data can include recipe names, recipe step numbers, preventive maintenance indicators, operators, etc. The context data can refer to historical context data (e.g., context data related to previous processes executed on previous substrates) and / or current context data (e.g., context data related to the current process or future processes to be executed on the current substrate). The context data can further include and identify sensors related to specific subsystems of the process chamber.

[0027] The data store 140 can also store task data. The task data can include one or more sets of process steps to be performed on the substrate during the deposition process, and can include one or more settings associated with each process. For example, the task data for a deposition process can include a temperature setting for the process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor for the material of the film to be deposited on the substrate, and the like. In another example, the task data can include a control pressure at a defined pressure point for a flow value. The 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 the substrate).

[0028] In some embodiments, the data store 140 can store an expected profile, a thickness profile, and a corrections profile. The expected profile can include one or more data points related to a desired film profile that is expected to be created by a certain process recipe. In some embodiments, the expected profile can include the desired thickness of the film, the desired thickness of one or more layers of the film, and / or the desired thickness of one or more loops of the film. The thickness profile includes one or more data points related to the current film profile generated by the manufacturing equipment 124. For example, the thickness profile can include the measured thickness of the film, the measured thickness of one or more layers of the film, and / or the measured thickness of one or more loops of the film. The thickness profile can be measured using the metrology instrument 128. The corrections profile can include one or more adjustments or offsets to be applied to the parameters of the process chamber or process recipe. For example, the corrections profile can include adjustments to the deposition time for film layers and / or loops, adjustments to the temperature setting for the process chamber, adjustments to the pressure setting for the process chamber, adjustments to the flow rate setting for the precursor for the material contained in the film deposited on the substrate surface, adjustments to the power supplied to the process chamber, adjustments to the ratio of two or more settings, etc. The corrections profile can be generated by comparing the expected profile (e.g., the thickness profile expected to be generated by the process recipe) and determining the adjustments to be applied to the parameters of the process recipe to achieve the expected profile using an algorithm, a library of known defect patterns, etc. The corrections profile can be applied to steps related to deposition processes, etching processes, etc.

[0029] In some embodiments, data store 140 may be configured to store data that is not accessible to a user of the manufacturing system. For example, process data, spectral data, context data, etc. obtained for a substrate being processed in the manufacturing system may not be accessible to a user (e.g., an operator) of the manufacturing system. In some embodiments, all data stored in data store 140 may be inaccessible to a user of the manufacturing system. In other or similar embodiments, some portions of the data stored in data store 140 may be inaccessible to the user, while other portions of the data stored in data store 140 may be accessible to the user. 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 user (e.g., the data is encrypted using a secret encryption key). In other or similar embodiments, data store 140 may include multiple data stores in which data that is inaccessible to the user is stored in one or more first data stores and data that is accessible to the user is stored in one or more second data stores.

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

[0031] In some embodiments, the prediction system 110 includes a prediction server 112, a server machine 170, and a server machine 180. The prediction server 112, the server machine 170, and the server machine 180 can 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 application specific integrated circuit (ASIC) accelerator (e.g., a tensor processing unit (TPU)).

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

[0033] Server machine 180 can include a training engine 182, a validation engine 184, a selection engine 185, and / or a test engine 186. An engine can refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing device, etc.), software (e.g., 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 can refer to a model artifact created by the training engine 182 using training data that includes training inputs and corresponding target outputs (the correct answers for each training input, also referred to herein as a training set). The training engine 182 can find patterns in the training data that map the training inputs to the target outputs (the answers to be predicted) and provide a machine learning model 190 that captures these patterns. The machine learning model 190 can use one or more of statistical modeling, support vector machine (SVM), radial basis function (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithm (k-NN), linear regression, random forest, neural network (e.g., artificial neural network), etc.

[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. Artificial neural networks generally include a feature representation component with a classifier or regression layer that maps features to a desired output space. Convolutional neural networks (CNNs), for example, host multiple layers of convolutional filters. In the lower layers, pooling is performed, non-linearity can be addressed, and a multi-layer perceptron is typically added on top of the lower layers to map the top-level 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 non-linear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep neural networks can learn in supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) modes. Deep neural networks include a hierarchy of layers, and different layers learn different levels of representation corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. In plasma process tuning, for example, the raw input can be a process result profile (e.g., a thickness profile indicating one or more thickness values across the surface of a substrate), the second layer can construct feature data related to the status of one or more zones of a control element of the plasma process system (e.g., zone orientation, plasma exposure duration, etc.), and the 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). In particular, the deep learning process can learn on its own which features should be optimally placed at which level. The "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, a deep learning system has a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output.A CAP potentially represents a causal connection between an input and an output. In the case of a feedforward neural network, the depth of the CAP can be the depth of the network and can be the number of hidden layers + 1. In the case of a recurrent neural network where a signal can propagate through layers more than once, the CAP depth is potentially unlimited.

[0035] In one embodiment, 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-dependent relationships. An RNN can learn an input-output mapping that depends on both the current input and past inputs. The RNN will handle 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 speeds, 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 of a neural network can be achieved in a supervised learning fashion, which involves feeding a training data set consisting 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 weights of the network across all its layers and nodes so that the error is minimized. Repeating this process over many labeled inputs in the training data set in many applications results in a network that can produce correct outputs when presented with inputs different from those present in the training data set.

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

[0038] To effectuate training, the processing logic can input the (one or more) training data sets into one or more untrained machine learning models. The machine learning model can be initialized prior to inputting the first input into the machine learning model. The processing logic trains the (one or more) untrained machine learning models based on the (one or more) training data sets to generate one or more trained machine learning models that perform various operations as described above. Training can be performed by inputting one or more of the sensor data one by one into the machine learning model.

[0039] The machine learning model processes the input and generates an output. An artificial neural network includes an input layer consisting of values at data points. The next layer is called a hidden layer, and the 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 non-linear mathematical transformation) to produce 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 the output values from the nodes in the previous layer, and each node applies weights to those values and then generates its own output value. This can be performed at each layer. The final layer is the output layer, and there is one node for each class, prediction, and / or output that the machine learning model can produce.

[0040] Accordingly, the output can include one or more predictions or inferences. For example, the output prediction or inference can include one or more predictions such as film deposition on a chamber component, erosion of a chamber component, a predicted failure of a chamber component, etc. The processing logic determines an error (i.e., a classification error) based on a difference between an output of a machine learning model (e.g., a prediction or inference) and a target label associated with the input training data. The processing logic adjusts the weights of one or more nodes in the machine learning model based on the error. An error term or delta can be determined for each node in an 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 a node) for one or more of its nodes. The parameters can be updated in a backpropagation fashion, so that the nodes in the topmost layer are updated first, followed by the nodes in the next layer, and so on. The artificial neural network includes multiple layers of "neurons", and each layer receives values as inputs from the neurons in the previous layer. The parameters for each neuron include weights associated with the values received from each of the neurons in the previous layer. Accordingly, adjusting the parameters can include adjusting the 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 data set, a target change in parameters for 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 level of accuracy has been achieved. The threshold level of accuracy can be, for example, 70%, 80%, or 90% accuracy. In one embodiment, the stopping criterion is met when the improvement in the accuracy of the machine learning model has stopped. 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, a reserved portion of the training data set can be used to test the model.

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

[0043] The verification engine 184 may be able to verify the machine learning model 190 using a corresponding set of features of the verification set from the training set generator 172. Once the model parameters are optimized, model verification can be performed to determine whether the model has improved and to determine the current accuracy of the deep learning model. The verification engine 184 can determine the accuracy of the machine learning model 190 based on a corresponding set of features of the verification set. The verification engine 184 can discard a trained machine learning model 190 that has an accuracy that does not meet the threshold accuracy. In some embodiments, the selection engine 185 may be able to select a trained machine learning model 190 that has an accuracy that meets the threshold accuracy. In some embodiments, the selection engine 185 may be able to select the trained machine learning model 190 that has the highest accuracy among the trained machine learning models 190.

[0044] The test engine 186 may be able to test the trained machine learning model 190 using a corresponding set of features of the test set from the training set 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 test engine 186 can determine the trained machine learning model 190 having the highest accuracy among all of the trained machine learning models based on the test set.

[0045] As described in detail below, the prediction server 112 includes a prediction component 114 that can operate the trained machine learning model 190 on 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 device 120, manufacturing equipment 124, sensor 126, measuring device 128, prediction server 112, data store 140, server machine 170, and server machine 180 may be coupled to each other via network 130. In some embodiments, network 130 is a public network that provides client device 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 device 120 with access to manufacturing equipment 124, measuring device 128, data store 140, and other privately available computing devices. 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.

[0047] Note that in some other embodiments, the functions of server machines 170 and 180, as well as prediction server 112, may be provided by a smaller number of machines. For example, in some embodiments, server machines 170 and 180 may be integrated into a single machine, and in some other or similar embodiments, server machines 170 and 180, as well as prediction server 112, may be integrated into a single machine.

[0048] Generally, the functions 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. Further, the functions attributed to a particular component may be performed by different or multiple components that operate together.

[0049] In an embodiment, a "user" can be represented as a single individual. However, other embodiments of the present disclosure include that the "user" is an entity controlled by multiple users and / or automated sources. For example, a set of individual users integrated as a group of administrators can be regarded as a "user".

[0050] In some embodiments, the manufacturing system can include two or more process chambers. For example, the exemplary manufacturing system 200 of FIG. 2 shows a plurality of process chambers 214, 216, 218. Note that in some embodiments, the data obtained for training a machine learning model and the data collected for being provided as an input to the machine learning model can be related to the same process chamber of the manufacturing system. In other or similar embodiments, the data obtained for training a machine learning model and the data collected for being provided as an input to the machine learning model can be related to different process chambers of the manufacturing system. In other or similar embodiments, the data obtained for training a machine learning model can be related to the process chamber of a first manufacturing system, and the data collected for being provided as an input to the machine learning model can be related to the process chamber of a second manufacturing system.

[0051] FIG. 2 is a top schematic view of an exemplary manufacturing system 200 according to an aspect of the present disclosure. The manufacturing system 200 can perform one or more processes on a substrate 202. The substrate 202 can be any suitable rigid, fixed-dimension, planar article suitable for manufacturing an electronic device or circuit component thereon, such as a silicon-containing disk or wafer, a patterned wafer, a glass plate, etc.

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

[0053] The process chambers 214, 216, 218 can be adapted to perform any number of processes on the substrate 202. The same or different substrate processes can occur in each of the processing chambers 214, 216, 218. Substrate processes can include, for example, 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 substrates therein. The process chambers 214, 216, 218 can each include one or more sensors configured to capture data about the substrate 202 before, after, or during the substrate process. For example, one or more sensors can be configured to capture spectral and / or non-spectral data about a portion of the substrate 202 during the substrate process. In other or similar embodiments, one or more sensors can be configured to capture data related to the environment within the process chambers 214, 216, 218 before, after, or during the substrate process. For example, one or more sensors can be configured to capture data related to the temperature, pressure, gas concentration, etc. of the environment within the process chambers 214, 216, 218 during the substrate process.

[0054] The load lock 220 can also be coupled to the housing 208 and the transfer chamber 210. The load lock 220 can be configured to interface with and couple to the transfer chamber 210 and the factory interface 206 on one side. In some embodiments, the load lock 220 can have an environmentally controlled atmosphere that can be changed from a vacuum environment (where substrates can be transferred in and out of the transfer chamber 210) to an inert gas environment at or near atmospheric pressure (where substrates can be transferred in and out of the factory interface 206). The factory interface 206 can be any suitable enclosure, such as, for example, an equipment front-end module (EFEM). The factory interface 206 can be configured to receive the substrate 202 from a substrate carrier 222 (such as a front-opening unified pod (FOUP)) that docks at various load ports 224 of the factory interface 206. The factory interface robot 226 (shown in dashed lines) can be configured to transfer the substrate 202 between the carrier 222 (also referred to as a container) and the load lock 220. The carrier 222 can be a substrate storage carrier or a spare part storage carrier.

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

[0056] The manufacturing system 200 can also include a system controller 228. The system controller 228 is a computing device, such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, etc., and / or can include the same. 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 dedicated 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), main memory, 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 processes in the manufacturing system 200 according to a process recipe. The instructions can be stored in a computer-readable storage medium that can include main memory, static memory, secondary storage, and / or the processing device (during execution of the instructions).

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

[0058] FIG. 3 is a schematic side cross-sectional 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, 218 described with respect to FIG. 2. The process chamber 300 can be used for processes 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, etc. 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 that seals an internal 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 nozzles in some embodiments, or by a plurality of 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 sidewalls 308 and a bottom 310. An exhaust port 326 can be defined in the chamber body 302 and can couple the internal volume 306 to a pump system 328. The pump system 328 can include one or more pumps and throttle valves utilized to evacuate and regulate the pressure of the internal volume 306 of the process chamber 300.

[0059] The showerhead 330 can be supported on the sidewall 308 of the chamber body 302. The showerhead 330 (or lid) can be opened to allow access to the interior volume 306 of the process chamber 300 and can provide a seal to the process chamber 300 while closed. The gas panel 358 can be coupled to the process chamber 300 to provide process and / or purge gases to the interior volume 306 through the showerhead 330 or lid and nozzles (e.g., through apertures in the showerhead or lid and nozzles). For example, the gas panel 358 can provide precursors for the materials of the film 351 deposited on the surface of the substrate 302. In some embodiments, the precursors can include silicon-based precursors or boron-based precursors. The showerhead 330 can include a gas distribution plate (GDP) and can have a plurality of gas delivery holes (also referred to as channels) 332 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 holds the substrate 302 during processing (e.g., during a deposition process), for example, using an electrostatic chuck 350.

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

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

[0062] In operation 410, the processing logic identifies a process recipe. In some embodiments, the processing logic can receive user input that identifies a process recipe. In other embodiments, the processing logic can automatically select a process recipe. For example, the processing logic can identify a process recipe for which at least one process run has ended and a thickness profile has been generated. In yet another embodiment, the processing logic can identify a process recipe based on the currently executing process recipe. For example, the processing logic can execute a deposition process on a substrate according to a process recipe. The deposition process can be executed in one or more process chambers. The process recipe can include one or more set parameters for the deposition process. For example, the set parameters can include deposition time for each layer and / or loop of the process recipe, temperature settings for the process chamber, pressure settings for the process chamber, flow rate settings for precursors for materials included in a film deposited on the substrate surface, showerhead height, and the like. The deposition process can deposit a plurality of layers on the substrate. For example, the deposition process can deposit alternating layers of oxide and nitride, alternating oxide and polysilicon layers, and the like.

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

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

[0065] In process 416, the processing logic generates a correction profile based on an expected profile and a thickness profile. The correction profile can include one or more correction actions to be applied to the parameters of the process recipe or the process chamber during one or more steps of the process recipe. In particular, the correction profile can include adjustment of the deposition time for each of one or more layers and / or one or more loops, adjustment of the temperature setting for the process chamber, adjustment of the pressure setting for the process chamber, adjustment of the flow rate setting for the precursor for the material contained in the film deposited on the substrate surface, adjustment of the power supplied to the process chamber, adjustment of the ratio of two or more settings, and the like. For example, the correction profile can include adjustment of the deposition time for a loop of the process recipe. In some embodiments, the correction profile can include a set of parameter adjustments for each layer and / or loop of the process recipe. For example, the correction profile can include adjustment of the deposition time for the first loop, adjustment of the deposition time for the second loop, adjustment of the deposition time for the third loop, and so on up to the final loop. Each adjustment can be applied to the respective deposition step to adjust the thickness of one or more loops or layers, and thus the film stack thickness can be made 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 such that the actual film thickness after loop 40 is equal to the expected film thickness (e.g., 20,500 nm) (e.g., decrease the deposition time for loop 40 by a certain time period).

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

[0067] In step 418, the processing logic generates an updated process recipe by applying the correction profile to the process recipe. For example, the correction profile can be applied to one or more steps of the current deposition process. FIG. 6 is a table 600 showing an exemplary correction profile. As shown, column 610 can include the index of the loop of the deposition process, and column 620 can include the time offset value to be applied to the deposition time of each corresponding loop.

[0068] In step 420, the processing logic executes the deposition steps of the identified process recipe on the substrate according to the updated process recipe. For example, the processing logic can deposit a first set of film layers (or loops) on the substrate, determine the thickness profile of the deposited film, generate a correction profile for correcting defects detected during the 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 can be adjusted in real time or near real time. This process can 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 can be executed by processing logic that can include hardware (circuits, dedicated logic, etc.), software (such as that operating on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 500 can be executed 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 can be executed by one or more other machines not shown in the figures. In some aspects, one or more steps of method 500 can be executed by manufacturing equipment 124 and / or client device 122. FIG. 5 describes determining a correction profile using a curve fitting method, but those skilled in the art will understand that other methods, equations, and models can be used to generate the correction profile, including, but not limited to, regression analysis, least squares methods, etc.

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

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

[0072] In process 516, the processing logic generates a polynomial. For example, a cubic polynomial can be represented as y = ax 3 + bx 2 + cx + d, where (x, y) are coordinates and a, b, c, and d are constants. The cubic polynomial is used as an example, and polynomials of any degree can be used. In some embodiments, the polynomial uses, as the x variable, a set of predicted chamber wall residual thickness values (e.g., the chamber wall residual thickness after loop 1, the chamber wall residual thickness after loop 2, the chamber wall residual thickness after loop 3, etc.) and, as the y coordinate, time values (e.g., the predicted deposition thickness at different chamber wall residual thicknesses). The (x, y) values can be taken from the predicted profile. Using the set of (x, y) coordinates, the constants for the polynomial are determined. In some embodiments, the chamber wall residue thickness is the seasoning thickness plus the deposition thickness, where the deposition thickness is equal to the sum of the thicknesses of all previous loops. The seasoning thickness can include a layer (e.g., a silicon oxide layer) on the chamber wall before the 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 process 518, the processing logic uses the polynomial and the thickness profile to determine the (y) value for a particular loop. In particular, the processing logic can receive an input (e.g., a user-based input, an automatic input, etc.) indicating the layer or loop of the deposition process. The 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] In process 520, the processing logic generates a correction value based on the (y) value. In one embodiment, the correction value can be generated based on the following formula: correction value = (y[first loop] / y[current loop number]) * t step where t stepis the expected time of the selected loop.

[0075] In step 522, the processing logic inputs the correction value into the correction profile. The steps of method 500 can be executed for one or more of the remaining loops or layers of the process recipe.

[0076] Figures 7A - 7B are graphs showing measurement data from a deposition process according to the same recipe, in accordance with aspects of the present disclosure. In particular, FIG. 7A is a graph showing thickness values (represented along the y - axis) of a set of loops (represented along the x - axis) for four different locations (locations 710 - 716) along a substrate. The thickness values of each loop shown in FIG. 7A are not adjusted according to the correction profile. As can be seen, the thickness of each loop gradually increases. FIG. 7B is a graph showing the thickness values of each loop adjusted according to the 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] FIG. 8 is a block diagram showing 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), intranet, 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), tablet PC, set-top box (STB), personal digital assistant (PDA), cellular phone, web appliance, server, network router, switch or bridge, or any device capable of executing a series of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, the term "computer" shall 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.

[0078] In a further aspect, computer system 800 can 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 can communicate with each other via a bus 808.

[0079] The processing device 802 can 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 dedicated 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] The computer system 800 can further include a network interface device 822 (e.g., coupled to network 874). The computer system 800 can also include a video display unit 810 (e.g., an LCD), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse), and a signal generation device 820.

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

[0082] The instructions 826 can also be present, in whole or in part, within the volatile memory 804 and / or within the processing device 802 during its execution by the computer system 800, and thus, the volatile memory 804 and the processing device 802 can also constitute a machine-readable storage medium.

[0083] The computer-readable storage medium 824 is shown as a single medium in the illustrative example, but the term "computer-readable storage medium" is intended to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more sets of executable instructions. The term "computer-readable storage medium" is intended to include any tangible medium that can store or encode a set of instructions for execution by a computer and that can cause a computer to perform any one or more of the methods described herein. The term "computer-readable storage medium" is intended to include, without limitation, solid state memories, optical media, and magnetic media.

[0084] The methods, components, and functions described herein can be implemented by individual hardware components or can be integrated into the functions of other hardware components, such as ASICs, FPGAs, DSPs, or similar devices. Further, the methods, components, and functions can be implemented by firmware modules or functional circuits within hardware devices. Further, the methods, components, and functions can be implemented in any combination of hardware devices and computer program components or can be implemented by a computer program.

[0085] Unless otherwise specified, terms such as "receiving", "executing", "providing", "obtaining", "causing", "accessing", "determining", "adding", "using", "training", etc. refer to actions and processes performed or implemented by a computer system that manipulates data represented as physical (electronic) quantities in computer system registers and memories and transforms that data into other data similarly represented as physical quantities in a computer system memory or register, or in other such information storage, transmission, or display devices. Also, terms such as "first", "second", "third", "fourth", etc. used herein are meant as labels to distinguish different elements and may not have a sequential meaning based on their numerical representation.

[0086] The examples described herein also relate to an apparatus for implementing the methods described herein. This apparatus can be specially constructed to implement the methods described herein or, alternatively, the apparatus can include a general-purpose computer system selectively programmed by a computer program stored in the computer system. Such a computer program can be stored on a computer-readable tangible storage medium.

[0087] The methods and exemplary examples described herein are, in essence, not related to a particular computer or other device. Various general-purpose systems can be used in accordance with the teachings described herein, or it may prove convenient to construct more specialized devices for performing each of the methods described herein and / or their individual functions, routines, subroutines, or steps. Examples of structures for various such systems are described in the above explanation.

[0088] The foregoing description is illustrative, not limiting. Although the present disclosure has been described with reference to specific exemplary examples and embodiments, it will be recognized that the present disclosure is not limited to the examples and embodiments described. The scope of the present disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled.

Claims

Claims 1. Executing a first deposition process among a plurality of deposition processes on a substrate according to a process recipe; Obtaining measurement data related to a first set of layers formed by the first deposition process; Obtaining an expected profile related to the process recipe, the expected profile including a plurality of values indicating desired thicknesses for a plurality of layers of the process recipe; Generating a correction profile including deposition time offset values for at least one layer among the plurality of layers based on the measurement data and the expected profile; Generating an updated process recipe by applying the correction profile to the process recipe; Executing a second deposition process among the plurality of deposition processes on the substrate according to the updated process recipe; And the second deposition process forms a second set of layers on the first set of layers. A method. Claims 2. The method according to claim 1, wherein the measurement data indicates the actual thickness of one or more deposited layers. Claims 3. The method according to claim 1, wherein the correction profile further includes one or more correction actions to be applied to one or more set parameters of the process recipe. Claims 4. Generating the correction profile includes: 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. The method according to claim 1. Claims 5. Generating the correction profile includes: Inputting the measurement data into a trained machine learning model; Obtaining an output value of the trained machine learning model, the output value indicating the correction profile. The method according to claim 1. Claims 6. The method according to claim 1, wherein the correction profile includes deposition time offset values for pairs of layers including various materials. Claims 7. The method according to claim 1, wherein the deposition time offset value matches the actual film stack thickness generated according to the process recipe to the expected film stack thickness generated according to the process recipe. Claims 8. A memory device, and a processing device operably coupled to the memory device to execute a process, comprising an electronic device manufacturing system, wherein the process comprises: executing a first deposition process among a plurality of deposition processes on a substrate according to a process recipe; acquiring measurement data related to a first set of layers formed by the first deposition process; acquiring an expected profile related to the process recipe, the expected profile including a plurality of values indicating desired thicknesses for a plurality of layers of the process recipe; generating a correction profile including deposition time offset values for at least one layer among the plurality of layers based on the measurement data and the expected profile; generating an updated process recipe by applying the correction profile to the process recipe; executing a second deposition process among the plurality of deposition processes on the substrate according to the updated process recipe including, wherein the second deposition process forms a second set of layers on the first set of layers, an electronic device manufacturing system.

9. The electronic device manufacturing system according to claim 8, wherein the measurement data indicates actual thicknesses of one or more deposited layers.

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

11. Generating the correction profile includes the processing device 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 including executing a process including, the electronic device manufacturing system according to claim 8.

12. Generating the correction profile includes the processing device inputting the measurement data into a trained machine learning model; acquiring an output value of the trained machine learning model, the output value indicating the correction profile, acquiring the output value including executing a process including, the electronic device manufacturing system according to claim 8.

13. The electronic device manufacturing system according to claim 8, wherein the correction profile includes deposition time offset values for pairs of layers containing various materials.

14. The electronic device manufacturing system according to claim 8, wherein the deposition time offset value matches an actual film stack thickness generated according to the process recipe with an expected film stack thickness generated according to the process recipe.

15. A non-transitory computer-readable storage medium comprising instructions for performing steps when executed by a processing device operably coupled to a memory, the steps comprising: causing a first deposition process among a plurality of deposition processes to be executed on a substrate according to a process recipe; acquiring measurement data related to a first set of layers formed by the first deposition process; acquiring an expected profile related to the process recipe, the expected profile including a plurality of values indicating desired thicknesses for a plurality of layers of the process recipe; generating a correction profile based on the measurement data and the expected profile, the correction profile including deposition time offset values for at least one of the plurality of layers; generating an updated process recipe by applying the correction profile to the process recipe; causing a second deposition process among the plurality of deposition processes to be executed on the substrate according to the updated process recipe including, wherein the second deposition process forms a second set of layers on the first set of layers.

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

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

18. Generating the correction profile includes: 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 The non-transitory computer-readable storage medium according to claim 17, comprising performing a process including this **Claim 19** Generating the correction profile includes Inputting the measurement data into a trained machine learning model Obtaining an output value of the trained machine learning model, the output value indicating the correction profile The non-transitory computer-readable storage medium according to claim 17, comprising performing a process including this **Claim 20** The non-transitory computer-readable storage medium according to claim 17, wherein the correction profile includes deposition time offset values for pairs of layers containing various materials

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