Methods and mechanisms for adjusting film deposition parameters during substrate fabrication

Abstract

An electronic device manufacturing system can obtain metrology data associated with a deposition process performed on a substrate according to a process recipe, where the deposition process produces a plurality of layers on a surface of the substrate. The manufacturing system may further obtain an expected profile associated with the process recipe, wherein the expected profile includes a plurality of values indicative of a desired thickness of the plurality of layers of the process recipe. The manufacturing system may further generate a corrected profile based on the metrology data and the expected profile, wherein the corrected profile includes a deposition time offset value for at least one of the plurality of layers. The manufacturing system may further generate an updated process recipe by applying the corrected profile to the process recipe and cause the deposition step to be performed on the substrate according to the updated process recipe.

Description

Technical Field

[0001] The present disclosure relates to electronic components, and more particularly, to methods and mechanisms for adjusting film deposition parameters during substrate fabrication. Background Art

[0002] The product can be manufactured by performing one or more manufacturing processes using manufacturing equipment. For example, semiconductor manufacturing equipment can be used to manufacture semiconductor devices (e.g., substrates) via semiconductor manufacturing processes. The manufacturing equipment can deposit a multilayer film on the surface of the substrate according to a process recipe, and an etching process can be performed to form a complex pattern in the deposited film. For example, the manufacturing equipment can perform a chemical vapor deposition (CVD) process to deposit a replacement layer on the substrate. During this substrate manufacturing process, the thickness of each layer can vary due to the changing deposition parameters and changes in the processing chamber conditions (e.g., accumulation of contaminants, erosion of certain components, etc.). These changes are usually compensated by manually increasing or decreasing the deposition time of subsequent layers to maintain the required total thickness of the film stack. However, such processing is prone to errors and may result in manufacturing delays and defective products. Therefore, a system capable of automatically adjusting film deposition parameters is needed. 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. The simplified summary is neither intended to identify the key or critical elements of the disclosure, nor to delineate any scope of a particular implementation of the disclosure or any scope of the claims. The sole purpose of the simplified summary is to present some concepts of the disclosure in a simplified form as a prelude to a more detailed description presented later.

[0004] In an aspect of the present disclosure, an electronic device manufacturing system is capable of obtaining metrology data associated with a deposition process performed on a substrate according to a process recipe, wherein the deposition process produces a plurality of layers on a surface of the substrate. The manufacturing system may further obtain an expected profile associated with the process recipe, wherein the expected profile includes a plurality of values ​​indicating expected thicknesses of the plurality of layers of the process recipe. The manufacturing system may further generate a correction profile based on the metrology data and the expected profile, wherein the correction profile includes a deposition time offset value for at least one of the plurality of layers. The manufacturing system may further generate an updated process recipe by applying the correction profile to the process recipe, and cause 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. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0008] Figure 1 is a block diagram illustrating an example system architecture, according to certain embodiments.

[0009] Figure 2 is a schematic top view of an example manufacturing system according to some embodiments.

[0010] Figure 3 is a cross-sectional schematic side view of an example processing chamber of an example manufacturing system in accordance with certain embodiments.

[0011] Figure 4 is a flow chart of a method for adjusting a process recipe based on a correction profile, according to certain embodiments.

[0012] Figure 5 is a flow chart of a method for determining a correction profile for a process recipe using a curve fitting method, according to certain embodiments.

[0013] Figure 6 is a table showing example correction profiles, according to certain embodiments.

[0014] Figure 7A-7B is a graph showing metrology data from a deposition process according to aspects of the present disclosure.

[0015] Figure 8 is a block diagram illustrating a computer system according to some embodiments. DETAILED DESCRIPTION

[0016] Described herein is a technique related to methods and mechanisms for adjusting film deposition parameters during substrate manufacturing. Films may be deposited on the surface of a substrate during a deposition process (e.g., a deposition (CVD) process, an atomic layer deposition (ALD) process, etc.) performed in a processing chamber of a manufacturing system. For example, in a CVD process, a substrate is exposed to one or more precursors that react on the substrate surface to produce a desired deposit. The film may include one or more layers of a material formed during the deposition process, and each layer may include a specific thickness gradient (e.g., a thickness variation along the layer of the deposited film). For example, a first layer may be formed directly on the surface of a substrate (referred to as a near layer or near end of the film) and have a first thickness. After forming the first layer on the surface of the substrate, a second layer having a second thickness may be formed on the first layer. This process continues until the deposition process is completed and the final layer of the film is formed (referred to as a far layer or the far end of the film). The film may include alternating layers of different materials. For example, the 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 cycle. For example, the film may include 40 cycles (e.g., 40 sets of oxide-nitride layers).

[0017] 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 film surface, or any other process required to produce a finished substrate. The etching process may include exposing a high energy process gas (e.g., plasma) to the sample surface to decompose the material at the surface, which may then be removed by a vacuum system.

[0018] The process chamber may perform each substrate manufacturing process (e.g., a deposition process, an etching process, a grinding process, etc.) according to a process recipe. The process recipe defines a set of specific operations to be performed on the substrate during the process and may include one or more settings associated with each operation. For example, a deposition process recipe may include a temperature setting for the process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor of a material included in a film deposited on the substrate surface, etc. Thus, the thickness of each film layer may be associated with these process chamber settings.

[0019] Generally, a process recipe includes a set of loops (e.g., 40 loops), where the thickness of the layers of some loops may be different from the thickness of the layers of other loops. For example, a film stack may include 40 oxide-nitride loops (e.g., 80 layers, alternating 40 layers of oxide and 40 layers of nitride), where a first loop of the film stack has an oxide layer of a first thickness and a nitride layer of a second thickness, followed by the next nine loops of the film stack having an oxide layer of a third thickness and a nitride layer of a fourth thickness, and a final 30 loops of the film stack having an oxide layer of a fifth thickness and a nitride layer of a sixth thickness.

[0020] During this substrate manufacturing process, the thickness of each cycle may vary due to changing deposition parameters and changes in processing chamber conditions (e.g., accumulation of contaminants, erosion of certain components, etc.). Changes in layer thickness can cause the gas distribution plate to be closer to or farther away from the surface of the substrate, thereby affecting the plasma flow and / or temperature and causing further deformation of the film. In some manufacturing systems, these changes are compensated by manually increasing or decreasing the deposition time of subsequent cycles to maintain the required total thickness of the film stack. For example, if the thickness of the first cycle is greater than the thickness required by the processing recipe, the technician can manually reduce the deposition time of the second cycle in the processing recipe to produce a thinner cycle than required by the processing recipe. This manual process of calculating the "step time offset" for each cycle and entering the offset into a table can be a time-consuming and error-prone process, resulting in manufacturing delays, output losses, and / or defects in the film.

[0021] Aspects and embodiments of the present disclosure address these and other shortcomings of the prior art by generating a correction profile to adjust film deposition parameters during substrate fabrication. Specifically, a metrology device 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 device can use the metrology data to generate a thickness profile indicating one or more thickness values ​​across a surface of the substrate. The thickness profile can indicate the thickness of a film on the substrate. The metrology device can generate the thickness profile at different times during the manufacturing process. For example, the metrology device can generate metrology data after each layer of a deposited film stack, after each cycle of a deposited film stack, and the like.

[0022] The manufacturing system may obtain an expected profile for a process recipe. The expected profile may include values ​​indicating the expected thickness of a film, the expected thickness of one or more layers of the film, and / or the expected thickness of one or more cycles of the film. Using the thickness profile and the expected profile, the manufacturing system may generate a correction profile. The correction profile may include one or more adjustments or offsets (e.g., corrective actions) to be applied to a process recipe or a process chamber parameter. For example, the correction profile may include adjustments to the deposition time, the temperature setting of the process chamber, the pressure setting of the process chamber, the flow rate setting for the precursor, the power supplied to the process chamber, the ratio of two or more settings, etc. for a specific layer or cycle of the process recipe. In some embodiments, the manufacturing system may generate a correction profile using one or more formulas or mathematical models. For example, the processing logic may 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 of the step so that the film thickness at the end of the deposition is the same as the film thickness indicated by the process recipe.

[0023] In some embodiments, the processing logic may use a machine learning model or use an inference engine to generate a correction profile. The manufacturing system may then use the correction profile to adjust a process recipe parameter (e.g., deposition time) for one or more steps of a process recipe (e.g., one or more layers or cycles of a process recipe). This allows the manufacturing system to generate adjustments for a specific process step of a process recipe.

[0024] Aspects of the present disclosure result in a technical advantage of significantly reducing the time required to perform optimization of parameters of a process recipe. Aspects of the present disclosure further result in a technical advantage of significantly reducing the time required to detect problems or failures experienced by substrates during manufacturing processes, as well as improving energy consumption, etc. The present disclosure may also result in generating diagnostic data and performing corrective actions to avoid inconsistent and abnormal products, as well as unplanned user time or downtime.

[0025] Figure 1 An illustrative computer system architecture 100 according to aspects of the present disclosure is depicted. In some embodiments, the computer system architecture 100 may be included as part of a manufacturing system (such as a Figure 3The computer system architecture 100 includes a client device 120, a manufacturing equipment 124, a metrology device 128, a prediction server 112 (e.g., for generating prediction data, for providing model adaptation, for using a knowledge base, etc.), and a data storage 140. The prediction server 112 can be part of the prediction system 110. The prediction system 110 can further include server machines 170 and 180. The manufacturing equipment 124 can include a sensor 126, which is configured to capture data of a substrate being processed at the manufacturing system. In some embodiments, the manufacturing equipment 124 and the sensor 126 can be part of a sensor system, which includes a sensor server (e.g., a field service server (FSS) at a manufacturing facility) and a sensor identifier reader (e.g., a front opening wafer transport box (FOUP) radio frequency identification (RFID) reader for the sensor system). In some embodiments, the metrology device 128 can be part of a metrology system, which includes a metrology server (e.g., a metrology database, a metrology folder, etc.) and a metrology identifier reader (e.g., a FOUP RFID reader for the metrology system).

[0026] Manufacturing equipment 124 may produce products, such as electronic devices, according to a recipe or by executing a run over a period of time. Manufacturing equipment 124 may include a processing chamber, such as a Figure 4 The processing chamber 400 described. The manufacturing equipment 124 can perform processing on a substrate (e.g., a wafer, etc.) in the processing chamber. Examples of substrate processing include deposition processing of depositing one or more layers of film on the surface of the substrate, etching processing of forming a pattern on the surface of the substrate, etc. The manufacturing equipment 124 can perform each processing according to a processing recipe. The processing recipe defines a set of specific operations to be performed on the substrate during the processing, and may include one or more settings associated with each operation. For example, a deposition process recipe may include a temperature setting for the processing chamber, a pressure setting for the processing chamber, a flow rate setting for a precursor of a material included in a film deposited on the surface of the substrate, etc.

[0027] In some embodiments, the manufacturing equipment 124 includes a sensor 126 that is configured to generate data associated with a substrate processed at the manufacturing system 100. For example, a processing chamber may include one or more sensors that are configured to generate spectral or non-spectral data associated with a substrate before, during, and / or after performing a process (e.g., a deposition process) on the substrate. In some embodiments, the spectral data generated by the sensor 126 may indicate the concentration of one or more materials deposited on the surface of the substrate. The sensor 126 configured to generate spectral data associated with the substrate may include a reflectometer sensor, an ellipsometer sensor, a thermal spectrum sensor, a capacitive sensor, etc. The sensor 126 configured to generate non-spectral data associated with the substrate may include a temperature sensor, a pressure sensor, a flow rate sensor, a voltage sensor, etc. For further details on the description of the manufacturing equipment 124, refer to Figure 3 and Figure 4 supply.

[0028] In some embodiments, the sensor 126 provides sensor data (e.g., sensor values, features, tracking data) associated with the manufacturing equipment 124 (e.g., associated with the manufacturing of a corresponding product, such as a wafer, by the manufacturing equipment 124). The manufacturing equipment 124 may produce a product according to a recipe or by performing an operation over a period of time. The sensor data received over a period of time (e.g., corresponding to at least a portion of a recipe or operation) may be referred to as tracking data received from different sensors 126 over time (e.g., historical tracking data, current tracking data, etc.). The sensor data may include values ​​of one or more of temperature (e.g., heater temperature), spacing (SP), pressure, high frequency radio frequency (HFRF), voltage of an electrostatic chuck (ESC), current, material flow, power, voltage, etc. The sensor data may be associated with or indicate a manufacturing parameter, such as a hardware parameter, such as a setting or component (e.g., size, type, etc.) of the manufacturing equipment 124, or a processing parameter of the manufacturing equipment 124. The sensor data may be provided when the manufacturing equipment 124 performs a manufacturing process (e.g., a device reading when processing a product). The sensor data may be different for each substrate.

[0029] The metrology equipment 128 may provide metrology data associated with substrates processed by the manufacturing equipment 124. The metrology data may include values ​​of film attribute data (e.g., wafer space film characteristics), dimensions (e.g., thickness, height, etc.), dielectric constants, dopant concentrations, densities, defects, etc. In some embodiments, the metrology data may further include values ​​of one or more surface profile attribute data (e.g., etch rate, etch rate uniformity, critical dimensions of one or more features included on the surface of the substrate, critical dimension uniformity across the surface of the substrate, edge placement errors, etc.). The metrology data may be for finished products or semi-finished products. The metrology data may be different for each substrate. The metrology data may be generated using, for example, reflectometry techniques, ellipsometry techniques, TEM techniques, etc.

[0030] In some embodiments, the metrology device 128 can be included as part of the manufacturing apparatus 124. For example, the metrology device 128 can be included within or coupled to a processing chamber and configured to generate metrology data of a substrate before, during, and / or after a process (e.g., a deposition process, an etching process, etc.) while the substrate remains in the processing chamber. In some cases, the metrology device 128 can be referred to as an in-situ metrology device. In another example, the metrology device 128 can be coupled to another station of the manufacturing apparatus 124. For example, the metrology device can be coupled to a transfer chamber, such as Figure 3 A transfer chamber 310 , a load lock, such as load lock 320 , or a factory interface, such as factory interface 306 .

[0031] The client device 120 may include a computing device, such as a personal computer (PC), a laptop, a mobile phone, a smart phone, a tablet computer, a netbook computer, a networked television ("smart TV"), a networked media player (e.g., a Blu-ray player), a set-top box, an over-the-top (OTT) streaming device, an operating 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), wherein the GUI enables a user to provide metrology measurements of substrates processed at the manufacturing system as input. The client device 120 may include a corrective action component 122. Corrective action component 122 may receive user input of an indication associated with manufacturing equipment 124 (e.g., via a graphical user interface (GUI) displayed by client device 120). In some embodiments, corrective action component 122 transmits the indication to prediction system 110, receives output (e.g., prediction data) from prediction system 110, determines a corrective action based on the output, and causes the corrective action to be implemented. In some embodiments, corrective action component 122 receives an indication of a corrective action from prediction system 110 and causes the corrective action to be implemented. Each client device 120 may include an operating system that allows a user to generate, view, or edit one or more of data (e.g., an indication associated with manufacturing equipment 124, a corrective action associated with manufacturing equipment 124, etc.).

[0032] The data storage 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 storage 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 storage 140 may store data associated with processing a substrate at the manufacturing equipment 124. For example, the data storage 140 may store data (referred to as processing data) collected by the sensor 126 at the manufacturing equipment 124 before, during, or after processing of the substrate. The processing data may refer to historical processing data (e.g., processing data generated for a previous substrate processed at the manufacturing system) and / or current processing data (e.g., processing data generated for a current substrate processed at the manufacturing system). The data storage may also store spectral data or non-spectral data associated with a portion of the substrate processed at the manufacturing equipment 124. The spectral data may include historical spectral data and / or current spectral data.

[0033] The data storage 140 may also store contextual data associated with one or more substrates processed at the manufacturing system. The contextual data may include a recipe name, a recipe step number, a preventive maintenance indicator, an operator, etc. The contextual data may refer to historical contextual data (e.g., contextual data associated with a previous process performed on a previous substrate) and / or current process data (e.g., contextual data associated with a current process or a future process to be performed on a previous substrate). The contextual data may further include sensors that identify a particular subsystem of a processing chamber.

[0034] The data storage 140 may also store task data. Task data may include one or more sets of operations to be performed on a substrate during a deposition process, and may include one or more settings associated with each operation. For example, task data for a deposition process may include a temperature setting for a processing chamber, a pressure setting for a processing chamber, a flow rate setting for a precursor of a material of a film deposited on a substrate, and the like. In another example, task data may include controlling pressure at defined pressure points of flow values. Task data may 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 process or a future process to be performed on a substrate).

[0035] In some embodiments, the data storage 140 may store an expected profile, a thickness profile, and a correction profile. The expected profile may include one or more data points associated with an expected film profile expected to be produced by a certain process recipe. In some embodiments, the expected profile may include an expected thickness of the film, an expected thickness of one or more layers of the film, and / or an expected thickness of one or more cycles of the film, etc. The thickness profile includes one or more data points associated with the current film profile 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 cycles of the film, etc. The thickness profile may be measured using a metrology device 128. The correction profile may include one or more adjustments or offsets to be applied to a processing chamber or a processing recipe. For example, the correction profile may include an adjustment to the deposition time of the film layer and / or cycle, a temperature setting of the processing chamber, a pressure setting of the processing chamber, a flow rate setting of a precursor of a material included in the film deposited on the substrate surface, an adjustment to the power provided to the processing chamber, a ratio of two or more settings, etc. A 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 parameters to be applied to the process recipe to achieve the expected profile using algorithms, a library of known failure modes, etc. The correction profile may be applied to steps associated with a deposition process, an etch process, etc.

[0036] In some embodiments, the data storage 140 may be configured to store data that is not accessible to users of the manufacturing system. For example, a user of the manufacturing system (e.g., an operator) may not be able to access processing data, spectral data, background data, etc. obtained for a substrate processed at the manufacturing system. In some embodiments, a user of the manufacturing system may not be able to access all of the data stored at the data storage 140. In other or similar embodiments, a portion of the data stored at the data storage 140 may be inaccessible to the user, while another portion of the data stored at the data storage 140 may be accessible to the user. In some embodiments, one or more portions of the data stored at the data storage 140 may be encrypted using an encryption mechanism unknown to the user (e.g., the data is encrypted using a private encryption key). In other or similar embodiments, the data storage 140 may include multiple data storages, wherein the data that is not accessible to the user is stored in one or more first data storages, and the data that is accessible to the user is stored in one or more second data storages.

[0037] In some embodiments, data storage 140 may be configured to store data associated with known failure modes. A failure mode may be one or more values ​​(e.g., vectors, scalars, etc.) associated with one or more problems or failures associated with a processing chamber subsystem. In some embodiments, a failure mode may be associated with a corrective action. For example, a failure mode may include a parameter adjustment step to correct the problem or failure indicated by the failure mode. For example, the prediction system may compare the determined failure mode with a library of known failure modes to determine the type of failure experienced by the subsystem, the cause of the failure, a recommended corrective action to correct the failure, etc.

[0038] 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 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.

[0039] 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) to train, validate, and / or test the machine learning model 190. The machine learning model 190 can be any algorithmic model that can learn from data. In some embodiments, the data set generator 172 can divide the training data into a training set, a validation set, and a test set. In some embodiments, the prediction system 110 generates multiple sets of training data.

[0040] 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 (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 is capable of training one or more machine learning models 190. The 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 the corresponding training inputs). The training engine 182 may find patterns in the training data that map the training inputs to target outputs (answers to be predicted) and provide a machine learning model 190 that captures these patterns. The machine learning model 190 may 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.

[0041] 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 typically includes a feature representation component with a classifier or regression layer that maps features to the desired output space. For example, a convolutional neural network (CNN) hosts multiple layers of convolution filters. Pooling is performed at the lower layer, and nonlinear problems can be solved. A multi-layer perceptron is usually attached on top of the lower layer to map the top features extracted by the convolution layer to a decision (e.g., classification output). Deep learning is a category of machine learning algorithms that uses a series of multiple layers of nonlinear processing units for feature extraction and transformation. Each consecutive layer uses the output of the previous layer as input. Deep neural networks can learn in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. A deep neural network includes a hierarchy of layers, in which 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 complex representation. For example, in plasma processing adjustment, the original input may be a processing result profile (e.g., a thickness profile indicating one or more thickness values ​​across the surface of a substrate); the second layer may constitute feature data associated with the state of one or more regions of a controlled component of a plasma processing system (e.g., the orientation of the region, plasma exposure duration, etc.); and the third layer may include a starting recipe (e.g., a recipe used as a starting point for determining an updated processing recipe that processes the substrate to produce a processing result that meets a threshold criterion). It should be noted that deep learning processing can learn by itself which features are best placed at which level. The "depth" in "deep learning" refers to the number of layers of data transformation. More precisely, deep learning systems have a considerable credit assignment path (CAP) depth. CAP is a chain of transformations from input to output. CAP describes the potential causal relationship between input and output. For a feedforward neural network, the depth of the CAP can be the depth of the network, and can be the number of hidden layers plus one. For a recurrent neural network where a signal can propagate through the layer more than once, the CAP depth is potentially infinite.

[0042] In one embodiment, one or more machine learning models are recurrent neural networks (RNNs). An RNN is a neural network that includes a memory that enables the neural network to capture temporal dependencies. An RNN is capable of learning input-output mappings that depend on current inputs and past inputs. The RNN will process past and future flow rate measurements and make predictions based on this continuous metering information. The RNN can be trained using a training data set to produce a fixed number of outputs (e.g., determining a set of substrate processing rates, determining modifications to a substrate processing recipe). One type of RNN that can be used is a long short-term memory (LSTM) neural network.

[0043] Training of 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 output, defining an error (by measuring the difference between the output and the label value), and using techniques such as deep gradient descent and backpropagation to adjust the network's weights in all its layers and nodes to minimize the error. In many applications, repeating this process over many labeled inputs in the training dataset results in a network that can produce the correct output when the input differs from the input present in the training dataset.

[0044] A training data set containing hundreds, thousands, tens of thousands, hundreds of thousands, or more sensor data and / or processing result data (eg, metrology data such as one or more thickness profiles associated with the sensor data) may be used to form the training data set.

[0045] To implement the training, the processing logic may input the training data set into one or more untrained machine learning models. The machine learning model may be initialized before the first input is input into the machine learning model. The processing logic trains the untrained machine learning model based on the training data set to generate one or more trained machine learning models that perform various operations as described above. The training may be performed by inputting one or more sensor data into the machine learning model at a time.

[0046] Machine learning models process inputs to produce outputs. Artificial neural networks include an input layer consisting of values ​​in data points. The next layer is called a hidden layer, and each node in the hidden layer receives one or more input values. Each node contains parameters (such as weights) applied to the input values. Therefore, each node essentially inputs the input values ​​into a multivariate function (for example, a nonlinear 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 output values ​​from the nodes in the previous layer, and each node applies weights to these values ​​and then produces its own output value. This can be performed at each layer. The last layer is the output layer, in which there is a node for each category, prediction, and / or output that the machine learning model can produce.

[0047] Thus, the output may include one or more predictions or inferences. For example, the output predictions or inferences may include one or more predictions of film buildup on chamber components, corrosion of chamber components, predicted failure of chamber components, and the like. The processing logic determines an error (i.e., a classification error) based on the difference between the output (e.g., prediction or inference) of the machine learning model and the target label associated with the input training data. The processing logic adjusts the weights of one or more nodes in the machine learning model according to the error. An error term or increment may be determined for each node in the artificial neural network. Based on this error, the artificial neural network adjusts one or more parameters of one or more of its nodes (the weights of one or more inputs of the node). The parameters may be updated in a back-propagation manner, so that the nodes of the highest layer are updated first, followed by the nodes of the next layer, and so on. The artificial neural network includes multiple layers of "neurons", each of which receives as input values ​​from neurons in the previous layer. The parameters of each neuron include weights associated with the values ​​received from each neuron in the previous layer. Thus, adjusting the parameters may include adjusting the weights of each input assigned to one or more neurons of one or more layers in the artificial neural network.

[0048] After one or more rounds of training, the processing logic may determine whether a stopping criterion has been met. The stopping criterion may be a target level of accuracy, a target number of processed images from the training data set, a target amount of parameter change from 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 may be, for example, 70%, 80%, or 90% accuracy. In one embodiment, the stopping criterion is met if the accuracy of the machine learning model has stopped improving. If the stopping criterion has not been met, further training is performed. If the stopping criterion is met, the training may be completed. Once the machine learning model has been trained, a retained portion of the training data set may be used to test the model.

[0049] Once one or more trained machine learning models 190 are generated, they can be stored in the prediction server 112 as a prediction component 114 or as a component of a prediction component 114.

[0050] The validation engine 184 can validate the machine learning model 190 using the corresponding feature set of the validation set from the training set generator 172. Once the model parameters are optimized, model validation can be performed to determine whether the model has improved and to determine the current accuracy of the deep learning model. The validation engine 184 can determine the accuracy of the machine learning model 190 based on the corresponding feature set of the validation set. The validation engine 184 can discard trained machine learning models 190 with an accuracy that does not meet a threshold accuracy. In some embodiments, the selection engine 185 can select a trained machine learning model 190 with an accuracy that meets a threshold accuracy. In some embodiments, the selection engine 185 can select a trained machine learning model 190 with the highest accuracy among the trained machine learning models 190.

[0051] The testing engine 186 can test the trained machine learning model 190 using the corresponding feature set of the test set from the dataset generator 172. For example, the first set of features of the test set can be used to test the first trained machine learning model 190 trained using the first set of features of the training set. The testing engine 186 can determine the trained machine learning model 190 with the highest accuracy among all the trained machine learning models based on the test set.

[0052] As described in detail below, the prediction server 112 includes a prediction component 114 that is capable of providing correction profiles generated using one or more formulas, and / or running a trained machine learning model 190 on current sensor data input to obtain one or more outputs indicative of correction or adjustment data (e.g., deposition time adjustment data for each layer and / or cycle of a process recipe).

[0053] The client devices 120, the manufacturing equipment 124, the sensors 126, the metering equipment 128, the prediction server 112, the data storage 140, the server machines 170, and the server machines 180 may be coupled to each other via a network 130. In some embodiments, the network 130 is a public network that provides access to the prediction server 112, the data storage 140, and other publicly available computing devices for the client devices 120. In some embodiments, the network 130 is a private network that provides access to the manufacturing equipment 124, the metering equipment 128, the data storage 140, and other privately available computing devices for the client devices 120. The 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.

[0054] It should be noted that in some other implementations, the functionality of server machines 170 and 180 and prediction server 112 may be provided by a smaller number of machines. For example, in some embodiments, server machines 170 and 180 may be integrated into a single machine, while in some other or similar embodiments, server machines 170 and 180 and prediction server 112 may be integrated into a single machine.

[0055] Generally speaking, 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. Furthermore, functions attributed to a particular component may be performed by different or multiple components operating together.

[0056] In an embodiment, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass a "user" as an entity controlled by multiple users and / or automation sources. For example, a group of individual users united as a group of administrators may be considered a "user."

[0057] In some embodiments, a manufacturing system may include more than one processing chamber. Figure 2 The example manufacturing system 200 shows multiple processing chambers 214, 216, 218. It should be noted that in some embodiments, the data obtained for training the machine learning model and the data collected to provide as input to the machine learning model can be associated with the same processing chamber of the manufacturing system. In other or similar embodiments, the data obtained for training the machine learning model and the data collected to provide as input to the machine learning model can be associated with different processing chambers of the manufacturing system. In other or similar embodiments, the data obtained for training the machine learning model can be associated with a processing chamber of a first manufacturing system, and the data collected to provide as input to the machine learning model can be associated with a processing chamber of a second manufacturing system.

[0058] Figure 2 2 is a top view schematic diagram of an example manufacturing system 200 according to aspects of the present disclosure. Manufacturing system 200 can perform one or more processes on substrate 202. Substrate 202 can be any suitable rigid, fixed-size planar article, such as a silicon-containing disk or wafer, a patterned wafer, a glass plate, etc., suitable for manufacturing electronic devices or circuit components thereon.

[0059] The manufacturing system 200 may include a processing tool 204 and a factory interface 206 coupled to the processing tool 204. The processing tool 204 may include a housing 208 having a transfer chamber 210 therein. The transfer chamber 210 may include one or more processing chambers (also referred to as process chambers) 214, 216, 218 arranged around and coupled thereto. The process chambers 214, 216, 218 may be coupled to the transfer chamber 210 via corresponding ports (e.g., slit valves, etc.). 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, wherein each arm includes 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 trays, sensor tools, etc.

[0060] The processing chambers 214, 216, 218 may be adapted to perform any number of processes on the substrate 202. The same or different substrate processes may be performed in each of the processing chambers 214, 216, 218. The 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 substrates therein. The processing chambers 214, 216, 218 may each include one or more sensors, and the one or more sensors are configured to capture data of the substrate 202 before, after, or during the substrate processing. For example, the one or more sensors may be configured to capture spectral data and / or non-spectral data of a portion of the substrate 202 during the substrate processing. In other or similar embodiments, the one or more sensors may be configured to capture data related to the environment within the processing chambers 214, 216, 218 before, after, or during the substrate processing. For example, one or more sensors may be configured to capture data associated with the temperature, pressure, gas concentration, etc. of the environment within the processing chambers 214, 216, 218 during substrate processing.

[0061] 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 be coupled to the transfer chamber 210 on one side and to the factory interface 206. The load lock 220 may have an environmentally controlled atmosphere that may change in some embodiments 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 housing, such as an equipment front end module (EFEM). The factory interface 206 may be configured to receive substrates 202 from substrate carriers 222 (e.g., front opening pods (FOUPs)) docked at respective load ports 224 of the factory interface 206. A factory interface robot 226 (shown in phantom) may be configured to transfer substrates 202 between a carrier (also referred to as a container) 222 and the load lock 220. The carrier 222 may be a substrate storage carrier or a replacement part storage carrier.

[0062] The manufacturing system 200 may also be connected to a client device (not shown) configured to provide information to a user (e.g., an operator) regarding the manufacturing system 200. In some embodiments, the client device may provide information to a user of the manufacturing 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 of a film to be deposited on a surface of the substrate 202 during a deposition process performed at the processing chambers 214, 216, 218. According to embodiments described herein, the client device may also provide information regarding modifications to a process recipe taking into account a corresponding set of deposition settings predicted to correspond to the target profile.

[0063] The manufacturing system 200 may also include a system controller 228. The system controller 228 may 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 may include one or more processing devices, which may be general-purpose processing devices such as microprocessors, central processing units, etc. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word set (VLIW) microprocessor, or a processor that implements other instruction sets or a processor that implements a combination of instruction sets. The processing device may also be one or more special-purpose processing devices such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. The system controller 228 may include a data storage device (e.g., one or more disk drives and / or solid-state drives), a main memory, a static memory, a network interface, and / or other components. The system controller 228 may execute instructions to perform any one or more of the methods and / or embodiments described herein. In some embodiments, the system controller 228 may execute instructions to perform one or more operations according to the process recipe at the manufacturing system 200. The instructions may be stored on a computer readable storage medium, which may include main memory, static memory, secondary storage, and / or a processing device (during execution of the instructions).

[0064] The system controller 228 may receive data from sensors included on or within various portions of the manufacturing system 200 (e.g., 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 may include spectral data and / or non-spectral data of a portion of the substrate 202. In other or similar embodiments, the data received by the system controller 228 may include data associated with processing the substrate 202 at the processing chambers 214, 216, 218, as previously described. For purposes of the present disclosure, the system controller 228 is described as receiving data from sensors included within the processing chambers 214, 216, 218. However, according to embodiments described herein, the system controller 228 may receive data from any portion of the manufacturing system 200 and may use the data received from the portion. In an illustrative example, the system controller 228 may receive data from one or more sensors of the processing chambers 214, 216, 218 before, after, or during processing of substrates at the processing chambers 214, 216, 218. The data received from the sensors of various parts 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 a data store related to the processing of the substrates. Figure 1 The described data is stored 140 .

[0065] Figure 3 is a schematic side view of a cross-section of a processing chamber 300 according to an embodiment of the present disclosure. In some embodiments, the processing chamber 300 may correspond to the reference Figure 2 The processing chamber 214, 216, 218 described above. The processing chamber 300 can be used for processing in which a corrosive plasma environment is provided. For example, the processing chamber 300 can be a chamber for a plasma etching machine or a plasma etching reactor, etc. In another example, the processing chamber can be a chamber for deposition processing, as previously described. In one embodiment, the processing chamber 300 includes a chamber body 302 and a showerhead 330 surrounding an internal space 306. The showerhead 330 may include a showerhead base and a showerhead gas distribution plate. Alternatively, in some embodiments, the showerhead 330 may be replaced by a cover and a nozzle, or in other embodiments by a plurality of pie-shaped showerhead compartments and a plasma generation unit. The chamber body 302 may be made of aluminum, stainless steel, or other suitable materials such as 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 the internal space 306 may be coupled to a pump system 328. The pump system 328 may include one or more pumps and throttle valves for evacuating and regulating the pressure of the interior space 306 of the process chamber 300 .

[0066] The showerhead 330 may be supported on the sidewall 308 of the chamber body 302. The showerhead 320 (or lid) may be opened to allow access to the interior space 306 of the processing chamber 300, and may provide a seal for the processing chamber 300 when closed. A gas panel 358 may be coupled to the processing chamber 300 to provide processing and / or cleaning gases to the interior space 306 through the showerhead 330 or the lid and nozzle (e.g., through the holes of the showerhead or the lid and nozzle). For example, the gas panel 358 may provide a precursor for the material of the film 351 deposited on the surface of the substrate 302. In some embodiments, the precursor may include a silicon-based precursor or a boron-based precursor. The showerhead 330 may include a gas distribution plate (GDP) and may have a plurality of gas delivery holes 332 (also referred to as channels) throughout the GDP. A substrate support assembly 348 is disposed in the interior space 306 of the processing chamber 300 below the showerhead 330. The substrate support assembly 348 holds the substrate 302 during processing (eg, during a deposition process) using, for example, an electrostatic chuck 350 .

[0067] In some embodiments, the processing chamber 300 may include a metrology device (not shown) configured to generate in-situ metrology measurements during a process performed at the processing chamber 300. The metrology device may be operably coupled to a system controller (e.g., system controller 328, as previously described). In some embodiments, the metrology device may be configured to generate metrology measurements (e.g., thickness) of the film 351 during a particular instance of the deposition process. The system controller may generate a thickness profile of the film 351 based on the metrology measurements received from the metrology device. In other or similar embodiments, the processing chamber 300 does not include a metrology device. In such embodiments, the system controller may receive one or more metrology measurements of the film 351 after the deposition process is completed at the processing chamber 300. The system controller may determine a deposition rate based on the one or more metrology measurements, and may generate a thickness profile of the film 351 based on the determined concentration gradient and the determined deposition rate of the deposition process.

[0068] Figure 4 4 is a flow chart of a method 400 for adjusting a process recipe based on a correction profile according to aspects of the present disclosure. The method 400 is performed by a processing logic that may include hardware (circuitry, dedicated logic, etc.), software (e.g., running on a general purpose computer system or a dedicated machine), firmware, or a combination thereof. In one implementation, the method 400 may be performed by a processor such as Figure 1 The method 400 may be performed by a computer system such as the computer system architecture 100 of the embodiment of the present invention. In other or similar embodiments, one or more operations of the method 400 may be performed by one or more other machines not depicted in the figure. In some aspects, one or more operations of the method 400 may be performed by the manufacturing equipment 124 and / or the client device 122.

[0069] At operation 410, processing logic identifies a processing recipe. In some embodiments, processing logic may receive user input identifying a processing recipe. In other embodiments, processing logic may automatically select a processing recipe. For example, processing logic may identify a processing recipe for which at least one processing run has ended and a thickness profile has been generated. In yet another embodiment, processing logic may identify a processing recipe based on a processing recipe currently being executed. For example, processing logic may perform a deposition process on a substrate according to the processing recipe. The deposition process may be performed in one or more processing chambers. The processing recipe may include one or more setup parameters for the deposition process. For example, the setup parameters may include a deposition time per layer and / or cycle of the processing recipe, a temperature setting for the processing chamber, a pressure setting for the processing chamber, a flow rate setting for a precursor of a material included in a film deposited on a surface of the substrate, a nozzle height, and the like. The deposition process may deposit multiple layers on a substrate. For example, the deposition process may deposit alternating oxide layers and nitride layers, alternating oxide layers and polysilicon layers, and the like.

[0070] At operation 412, processing logic obtains a thickness profile for the process recipe. The thickness profile includes one or more data values ​​associated with a 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 cycles of the film, etc. The thickness profile may be measured using the metrology equipment 128. In some embodiments, the thickness profile is retrieved from the data storage 140.

[0071] At operation 414 , processing logic obtains an expected profile for the process recipe. The expected profile may include an expected thickness of the film, an expected thickness of one or more layers of the film, and / or an expected thickness of one or more cycles of the film, etc. In some embodiments, the expected profile is retrieved from data storage 140 .

[0072] In operation 416, the 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 applied to the parameters of the processing recipe or the processing chamber during one or more steps of the processing recipe. Specifically, the correction profile may include the deposition time for each of one or more layers and / or one or more cycles, the temperature setting of the processing chamber, the pressure setting of the processing chamber, the flow rate setting of the precursor of the material included in the film deposited on the substrate surface, the power provided to the processing chamber, the ratio of two or more settings, etc. For example, the correction profile may include the deposition time adjustment for the cycle of the processing recipe. In some embodiments, the correction profile may include a set of parameter adjustments for each layer and / or cycle of the processing recipe. For example, the correction profile may include the deposition time adjustment for the first cycle, for the second cycle, for the third cycle, etc., until the final cycle. Each adjustment can be applied to the corresponding deposition step to adjust the thickness of one or more cycles 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 cycle 39 is a first value (e.g., 20,000 nm), the expected film thickness after cycle 40 is a second value (e.g., 20,500 nm), and the actual film thickness during the deposition run and after cycle 39 is a third value (e.g., 20,050), the adjustment profile can indicate a correction to the deposition time of cycle 40 (e.g., reducing the deposition time of cycle 40 by a specific period of time) so that the actual film thickness after cycle 40 is equal to the expected film thickness (e.g., 20,500 nm).

[0073] In some embodiments, processing logic may use one or more formulas or mathematical models to generate a correction profile. For example, processing logic may use data values ​​from an expected 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 Figure 5 In some embodiments, the processing logic may generate the correction profile using a machine learning model (e.g., machine learning model 190) or using an inference engine.

[0074] At operation 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 a current deposition process. Figure 6 6 is a table 600 illustrating an example correction profile. As shown, column 610 may include an index of a cycle of a deposition process, and column 620 may include a time offset value to be applied to the deposition time of each corresponding cycle.

[0075] At operation 420, processing logic performs 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 cycles) on the substrate, determine the thickness profile of the deposited film, generate a correction profile to correct any 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 cycles) 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.

[0076] Figure 5 is a flow chart of a method 500 for determining a correction profile for a process recipe using a curve fitting method according to aspects of the present disclosure. The method 500 is performed by a processing logic that may include hardware (circuitry, dedicated logic, etc.), software (e.g., running on a general purpose computer system or a dedicated machine), firmware, or a combination thereof. In one implementation, the method 500 may be performed by a processor such as Figure 1 In other or similar embodiments, one or more operations of method 500 may be performed by one or more other machines not depicted in the figure. In some aspects, one or more operations of method 500 may be performed by manufacturing equipment 124 and / or client device 122. Figure 5 The use of a curve fitting method to determine the correction profile is discussed. Those skilled in the art will appreciate that other methods, formulas, and models may be used to generate the correction profile, including but not limited to regression analysis, least squares method, and the like.

[0077] At operation 512, processing logic obtains a thickness profile for the process recipe. The thickness profile includes one or more thickness data values ​​associated with a 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 cycles of the film, etc. The thickness profile may be measured using the metrology equipment 128. In some embodiments, the thickness profile is retrieved from the data storage 140.

[0078] At operation 514 , processing logic obtains an expected profile for the process recipe. The expected profile may include an expected thickness of the film, an expected thickness of one or more layers of the film, and / or an expected thickness of one or more cycles of the film, etc. In some embodiments, the expected profile is retrieved from data storage 140 .

[0079] At operation 516, processing logic generates a polynomial equation. For example, a third-order polynomial equation may be represented as: y = ax 3 +bx 2+cx+d, where (x, y) are coordinates and a, b, c, and d are constants. As an example, a polynomial equation with a third order is used, and any order of polynomial equation can be used. In some embodiments, a set of expected chamber wall residual thickness values ​​are used as x variables (e.g., chamber wall residual thickness after cycle one, chamber wall residual thickness after cycle two, chamber wall residual thickness after cycle three, and so on), and a time value is used as the y coordinate (e.g., expected deposition thickness at different chamber wall residual thicknesses) to generate the polynomial equation. The (x, y) values ​​can be extracted from the expected profile. Using a set of (x, y) coordinates, the constants of the polynomial equation are determined. In some embodiments, the chamber wall residual thickness is the season thickness plus the deposition thickness, where the deposition thickness is equal to the total thickness of all previous cycles. The season thickness may include a layer (e.g., a silicon oxide layer) above the chamber wall before the substrate is introduced into the chamber for processing. The deposited season layer reduces the likelihood that contaminants will interfere with subsequent processing steps.

[0080] At operation 518, processing logic determines the value of (y) for a particular cycle using the polynomial equation and the thickness profile. Specifically, processing logic may receive input (e.g., based on user input, automatic input, etc.) indicating a layer or cycle of a deposition process. Processing logic may then input the actual thickness of the cycle or layer (obtained from the thickness profile) into the polynomial equation to calculate (y) for the cycle or layer.

[0081] At operation 520, processing logic generates a correction value based on the (y) value. In one embodiment, the correction value may be generated based on the following formula: Correction value = (y[first cycle] / y[current cycle number])*t step , where t step is the expected time for the selected cycle.

[0082] At operation 522, processing logic inputs the correction value into the correction profile.The operations of method 500 may be performed for one or more of the remaining cycles or layers of the process recipe.

[0083] Figure 7A-7B is a graph showing metrology data from a deposition process according to the same recipe according to aspects of the present disclosure. In particular, Fig. 7A is a graph showing thickness values ​​(shown along the y-axis) for a set of cycles (shown along the x-axis) at four different locations (locations 710 - 716 ) along a substrate. Fig. 7A The thickness values ​​for each cycle shown in are not adjusted according to the calibration profile. As shown in the figure, the thickness gradually increases with each cycle. Figure 7Bis a graph showing the thickness values ​​for each cycle adjusted according to the correction profile. As shown, the thickness for each cycle remains relatively the same throughout the deposition process and does not gradually increase.

[0084] Figure 8 800 is a block diagram illustrating a computer system 800 according to certain embodiments. In some embodiments, the computer system 800 may be connected (e.g., via a network, such as a local area network (LAN), an intranet, an extranet, or the Internet) to other computer systems. The computer system 800 may be run in the capacity of a server or client computer in a client-server environment, or as an endpoint computer (peercomputer) in a peer-to-peer or distributed network environment. The computer system 800 may be provided by a personal computer (PC), a tablet computer, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a network application, a server, a network router, a switch or a bridge, or any other device capable of executing a set of instructions (sequentially or otherwise) that designates an action to be taken by the device. In addition, the term "computer" shall include any collection of computers that execute a set (or multiple sets) of instructions, either individually or in combination, to perform any one or more of the methods described herein.

[0085] In a further aspect, the computer system 800 may include a processing device 802, a volatile memory 804 (e.g., a random access memory (RAM)), a non-volatile memory 806 (e.g., a read-only memory (ROM) or an electronically erasable programmable read-only memory (EEPROM)), and a data storage device 816, and the processing device 802, the volatile memory 804 (e.g., a random access memory (RAM)), the non-volatile memory 806 (e.g., a read-only memory (ROM) or an electronically erasable programmable read-only memory (EEPROM)), and the data storage device 816 can communicate with each other via a bus 808.

[0086] 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 set (VLIW) microprocessor, a microprocessor that implements other types of instruction sets, or a microprocessor that implements a combination of types of instruction sets) or a special-purpose processor (e.g., an application-specific integrated circuit (ASIC), a programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).

[0087] The computer system 800 may further include a network interface device 822 (e.g., coupled to the network 874). The 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 / or a signal generating device 820.

[0088] In some implementations, the data storage device 816 may include a non-transitory computer-readable storage medium 824 on which may be stored instructions 826 encoding any one or more of the methods or functions described herein, including instructions for Figure 1 The components of the present invention may be encoded with instructions (eg, corrective action component 122, prediction component 114, etc.) and used to implement the methods described herein.

[0089] The instructions 826 may also reside, completely or partially, within the volatile memory 804 and / or the processing device 802 during execution of the instructions 826 by the computer system 800 , and thus, the volatile memory 804 and the processing device 802 may also constitute machine-readable storage media.

[0090] Although computer-readable storage medium 824 is shown as a single medium in the illustrative example, the term "computer-readable storage medium" shall include a single medium or multiple media (e.g., a central or distributed database, and / or associated buffers and servers) that store one or more sets of executable instructions. The term "computer-readable storage medium" shall also include tangible media capable of storing or encoding a set of instructions executed by a computer, the set of instructions causing the computer to perform any one or more of the methods described herein. The term "computer-readable storage medium" shall include, but is not limited to, solid-state memories, optical media, and magnetic media.

[0091] The methods, components, and features described herein may be implemented by separate hardware components or integrated into the functionality of other hardware components such as ASICS, FPGAs, DSPs, or similar devices. In addition, the methods, components, and features may be implemented by firmware modules or functional circuits within a hardware device. In addition, the methods, components, and features may be implemented in any combination of hardware devices and computer program components or in a computer program.

[0092] Unless expressly stated otherwise, terms such as "receiving," "performing," "providing," "obtaining," "causing," "accessing," "determining," "increasing," "using," "training," and the like refer to actions and processes performed or implemented by a computer system to manipulate and transform data representing physical (electronic) quantities in computer system registers and memories into other data similarly representing physical quantities in computer system memories or registers or other such information storage, transmission, or display devices. In addition, the terms "first," "second," "third," "fourth," and the like as used herein are intended as labels to distinguish different elements and may have an ordinal meaning regardless of their numerical designation.

[0093] The examples described herein also relate to an apparatus for performing the methods described herein. The apparatus may be specially constructed for performing the methods described herein, or may comprise a general purpose computer system selectively programmed by a computer program stored in the computer system. The computer program may be stored in a computer-readable tangible storage medium.

[0094] The methods and illustrative examples described herein have no inherent relevance to any particular computer or other device. Various general purpose systems may be used in accordance with the teachings described herein, or it may prove convenient to construct more specialized equipment to perform the methods described herein and / or each of their respective functions, routines, subroutines, or operations. Examples of structures for various of these systems are set forth in the description above.

[0095] The above description is intended to be illustrative rather than restrictive. Although the present disclosure has been described with reference to specific illustrative examples and implementations, it should be appreciated that the present disclosure is not limited to the described examples and implementations. The scope of the present disclosure should be determined with reference to the following claims and the full range of equivalents granted by the claims.

Claims

1. A method comprising: obtaining metrology data associated with a deposition process performed on a substrate according to a process recipe, wherein the deposition process produces a plurality of layers on a surface of the substrate; obtaining an expected profile associated with the process recipe, wherein the expected profile includes a plurality of values ​​indicative of expected thicknesses of a plurality of layers of the process recipe; generating a corrected profile based on the metrology data and the expected profile, wherein the corrected profile includes 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 A deposition step is caused to be performed on the substrate according to the updated process recipe.

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

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

4. The method of claim 1 , wherein generating the correction profile comprises: generating a polynomial equation based on at least one of the metrology data or the expected profile; and One or more deposition time offset values ​​are determined using the polynomial equation.

5. The method of claim 1 , wherein generating the correction profile comprises: inputting the metrology data into a trained machine learning model; and An output value of the trained machine learning model is obtained, wherein the output value is indicative of the correction profile.

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

7. 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. An electronic device manufacturing system, comprising: Memory device; and A processing device, operatively coupled to the memory device, to perform the following operations, including: obtaining metrology data associated with a deposition process performed on a substrate according to a process recipe, wherein the deposition process produces a plurality of layers on a surface of the substrate; obtaining an expected profile associated with the process recipe, wherein the expected profile includes a plurality of values ​​indicative of expected thicknesses of a plurality of layers of the process recipe; generating a corrected profile based on the metrology data and the expected profile, wherein the corrected profile includes 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 A deposition step is caused to be performed on the substrate according to the updated process recipe.

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

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

11. The electronic device manufacturing system of claim 8, wherein generating the correction profile comprises the processing device performing the following operations, including: generating a polynomial equation based on at least one of the metrology data or the expected profile; and One or more deposition time offset values ​​are determined using the polynomial equation.

12. The electronic device manufacturing system of claim 8, wherein generating the correction profile comprises the processing device performing the following operations, including: inputting the metrology data into a trained machine learning model; and An output value of the trained machine learning model is obtained, wherein the output value is indicative of the correction profile.

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

14. 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 operations comprising: obtaining metrology data associated with a deposition process performed on a substrate according to a process recipe, wherein the deposition process produces a plurality of layers on a surface of the substrate; obtaining an expected profile associated with the process recipe, wherein the expected profile includes a plurality of values ​​indicative of expected thicknesses of a plurality of layers of the process recipe; generating a corrected profile based on the metrology data and the expected profile, wherein the corrected profile includes 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 A deposition step is caused to be performed on the substrate according to the updated process recipe.

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

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

18. The non-transitory computer-readable storage medium of claim 17, wherein generating the correction profile comprises the processing device performing the following operations, including: generating a polynomial equation based on at least one of the metrology data or the expected profile; and One or more deposition time offset values ​​are determined using the polynomial equation.

19. The non-transitory computer-readable storage medium of claim 17, wherein generating the correction profile comprises the processing device performing the following operations, including: inputting the metrology data into a trained machine learning model; and An output value of the trained machine learning model is obtained, wherein the output value is indicative of the correction profile.

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

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