Method and device for determining process parameters and semiconductor process equipment

Abstract

The embodiment of the invention provides a process parameter determination method and device and semiconductor process equipment, and the method comprises the steps: responding to target process information inputted by a user, and inputting the target process information to a target network model; the target process information comprises a process result which can be achieved by a to-be-determined process formula; predicting process parameters required for achieving the process result through the target network model; the target network model comprises a capsule network layer constructed based on a capsule network; and determining a prediction result output by the target network model as a process parameter of the to-be-determined process formula. According to the method, the process parameters meeting the process requirements can be rapidly determined, and a large amount of time is saved. Meanwhile, the target network model comprises the capsule network layer constructed based on the capsule network, so that the target network model integrates the characteristics of the capsule network, and the accuracy of the process parameters can be improved.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of production process technology, specifically, to process recipe processing technology under the field of production process technology, and more specifically, to a method and device for determining process parameters and semiconductor process equipment. Background Art

[0002] The process recipe can be simply understood as the combination of various process parameters under the working state of the process equipment. Its accuracy directly affects the process results. In the process of process generation, the process requirements, that is, the expected process results, usually appear first. Then, based on the process requirements, the process parameters are constantly adjusted so that the adjusted process parameters can meet the process requirements.

[0003] However, the above process cannot be completed overnight. It requires continuous adjustment of process parameters so that the process results of the adjusted process parameters continuously approach the expected process results, and finally obtain process parameters that meet the process requirements. The whole process is time-consuming. Summary of the invention

[0004] Multiple embodiments in this specification provide a method and device for determining process parameters and semiconductor process equipment to achieve the purpose of quickly determining process parameters that can meet process requirements.

[0005] In a first aspect, an embodiment of the present specification provides a method for determining a process parameter, the method for determining a process parameter comprising:

[0006] In response to target process information input by a user, the target process information is input into a target network model; wherein the target process information includes a process result that can be achieved by the process recipe to be determined;

[0007] Predicting the process parameters required to achieve the process result through the target network model; wherein the target network model includes: a capsule network layer constructed based on a capsule network;

[0008] The prediction results output by the target network model are determined as the process parameters of the process recipe to be determined.

[0009] In some embodiments, the capsule network layer includes: an input layer, a convolutional layer, a main capsule layer, a digital capsule layer, and an output layer;

[0010] Predicting the process parameters required to achieve the process result through the target network model includes:

[0011] Preprocessing the target process information through the input layer to generate input features;

[0012] Extracting features from the input features through the convolution layer to generate an initial feature vector;

[0013] Extracting features from the initial feature vector through the main capsule layer to generate an initial capsule vector;

[0014] Performing feature processing on the initial capsule vector through the digital capsule layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values;

[0015] The output layer outputs a predicted value with the maximum predicted probability for each process parameter based on the result capsule vector.

[0016] In some embodiments, extracting features from the initial feature vector through the main capsule layer to generate an initial capsule vector includes:

[0017] The initial feature vector is extracted by the main capsule layer in a dynamic routing manner to generate an initial capsule vector.

[0018] In some embodiments, before performing feature processing on the initial capsule vector through the digital capsule layer to generate a target number of result capsule vectors, the method further includes:

[0019] Determining an execution chamber based on the target process information through the target network model, wherein the execution chamber is a chamber in a process equipment that executes the process recipe to be determined;

[0020] The target network model is used to determine the number of process parameters corresponding to the execution chamber as the target number based on the correspondence between each chamber in the process equipment and the number of process parameters.

[0021] In some embodiments, the target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, wherein the input of the long short-term memory network layer is the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

[0022] In some embodiments, predicting the process parameters required to achieve the process result by the target network model includes:

[0023] Processing the target process information through the capsule network layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values;

[0024] The result capsule vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process result.

[0025] In some embodiments, the target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, and an attention module constructed based on an attention mechanism; wherein the input of the attention module is the target process information, the input of the long short-term memory network layer is a fusion result of the output of the attention module and the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

[0026] In some embodiments, predicting the process parameters required to achieve the process result by the target network model includes:

[0027] Processing the target process information through the capsule network layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values;

[0028] Processing the target process information through the attention module to generate a context vector corresponding to each of the result capsule vectors;

[0029] The fusion result of the result capsule vector and the context vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process result.

[0030] In some embodiments, in response to the target process information input by the user, before inputting the target process information into the target network model, the method further includes:

[0031] Acquire training data; wherein the training data includes: historical process recipes and process results that can be achieved by the historical process recipes;

[0032] The training data is used to perform model training on the network model constructed by the capsule network, and the trained network model is determined as the target network model.

[0033] In some embodiments, during the model training process of the network model constructed by the capsule network using the training data, the sum of the interval loss and the reconstruction loss is used as the model loss.

[0034] In a second aspect, an embodiment of the present specification provides a device for determining a process parameter, the device for determining a process parameter comprising:

[0035] A response module, configured to respond to target process information input by a user and input the target process information into a target network model; wherein the target process information includes a process result that can be achieved by the process recipe to be determined;

[0036] A model processing module, used to predict the process parameters required to achieve the process result through the target network model; wherein the target network model includes: a capsule network layer constructed based on a capsule network;

[0037] The determination module is used to determine the prediction results output by the target network model as the process parameters of the process recipe to be determined.

[0038] In some embodiments, the capsule network layer includes: an input layer, a convolutional layer, a main capsule layer, a digital capsule layer, and an output layer;

[0039] Model processing module, including:

[0040] A preprocessing unit, used for preprocessing the target process information through the input layer to generate input features;

[0041] A first feature extraction unit, configured to extract features from the input features through the convolution layer to generate an initial feature vector;

[0042] A second feature extraction unit, configured to extract features from the initial feature vector through the main capsule layer to generate an initial capsule vector;

[0043] a model processing unit, configured to perform feature processing on the initial capsule vector through the digital capsule layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values;

[0044] The model output unit is used to output a predicted value with the maximum predicted probability of each process parameter based on the result capsule vector through the output layer.

[0045] In some implementations, the second feature extraction unit is specifically configured to extract features from the initial feature vector in a dynamic routing manner through the main capsule layer to generate an initial capsule vector.

[0046] In some embodiments, the device further comprises:

[0047] A chamber determination module, configured to determine an execution chamber based on the target process information through the target network model, wherein the execution chamber is a chamber in a process equipment that executes the process recipe to be determined;

[0048] The quantity determination module is used to determine the quantity of process parameters corresponding to the execution chamber as the target quantity based on the correspondence between each chamber in the process equipment and the quantity of process parameters through the target network model.

[0049] In some embodiments, the target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, wherein the input of the long short-term memory network layer is the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

[0050] In some embodiments, the model processing module is specifically configured to:

[0051] Processing the target process information through the capsule network layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values;

[0052] The result capsule vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process result.

[0053] In some embodiments, the target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, and an attention module constructed based on an attention mechanism; wherein the input of the attention module is the target process information, the input of the long short-term memory network layer is a fusion result of the output of the attention module and the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

[0054] In some embodiments, the model processing module is specifically configured to:

[0055] Processing the target process information through the capsule network layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values;

[0056] Processing the target process information through the attention module to generate a context vector corresponding to each of the result capsule vectors;

[0057] The fusion result of the result capsule vector and the context vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process result.

[0058] In some embodiments, the device further comprises:

[0059] Model training module, used to:

[0060] Acquire training data; wherein the training data includes: historical process recipes and process results that can be achieved by the historical process recipes;

[0061] The training data is used to perform model training on the network model constructed by the capsule network, and the trained network model is determined as the target network model.

[0062] In some embodiments, during the model training process of the network model constructed by the capsule network using the training data, the sum of the interval loss and the reconstruction loss is used as the model loss.

[0063] In a third aspect, an embodiment of the present specification provides a semiconductor process equipment, including: a host computer and a slave computer connected to the host computer;

[0064] The upper computer is used to execute the method for determining the process parameters as described in any one of the first aspects to determine the process parameters of the process recipe to be determined, and send the process parameters of the process recipe to be determined to the lower computer;

[0065] The lower computer is used to receive the process parameters of the process recipe to be determined, and perform process production according to the process parameters of the process recipe to be determined.

[0066] In a fourth aspect, an embodiment of this specification provides an electronic device, including a processor and a memory;

[0067] Wherein, the memory is connected to the processor, and the memory is used to store a computer program;

[0068] The processor is used to implement the method for determining the process parameters as described in any one of the first aspects by running the computer program stored in the memory.

[0069] In a fifth aspect, an embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for determining the process parameters as described in any one of the first aspects is implemented.

[0070] In a sixth aspect, an embodiment of the present specification provides a computer program product or a computer program, wherein the computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; the processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, the steps of the method for determining the process parameters as described in any one of the first aspects are implemented.

[0071] In the multiple implementation methods provided in this specification, for the process parameters of the process recipe to be determined, the process results that can be achieved or expected to be achieved, that is, the process requirements, are first determined. Then, the process parameters that can achieve this process result are predicted with the help of the target network model, and finally the predicted process parameters are used as the process parameters of the process recipe to be determined. Compared with the self-consistent iterative method to reversely find the optimal process parameters, the present application can quickly determine the process parameters that can meet the process requirements, saving a lot of time. At the same time, the target network model includes a capsule network layer constructed based on the capsule network, so that the target network model integrates the characteristics of the capsule network, and can better take into account the correlation between different features, thereby improving the accuracy of the process parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A flow chart of a conventional method for determining process parameters provided for one embodiment of the present specification;

[0073] Figure 2 A schematic flow chart of a method for determining process parameters provided in one embodiment of this specification;

[0074] Figure 3 A schematic diagram of a dynamic routing process in a capsule network in one embodiment of this specification;

[0075] Figure 4 A network structure diagram of a long short-term memory network in one embodiment of this specification;

[0076] Figure 5 A schematic flow chart of a method for determining process parameters provided in yet another embodiment of this specification;

[0077] Figure 6 A practical application flow chart of a method for determining process parameters provided in one embodiment of this specification;

[0078] Figure 7 One of the application architecture diagrams of a method for determining process parameters provided in one embodiment of this specification;

[0079] Figure 8 A second application architecture diagram of a method for determining process parameters provided in one embodiment of this specification;

[0080] Fig. 9 A schematic diagram of a device for determining process parameters provided in accordance with an embodiment of the present specification;

[0081] Fig.10 A schematic diagram of the structure of an electronic device provided for one embodiment of the present specification. DETAILED DESCRIPTION

[0082] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of this specification shall have the common meanings understood by persons with ordinary skills in the field to which this specification belongs. The words "first", "second" and similar words used in the embodiments of this specification do not indicate any order, quantity or importance, but are only used to avoid confusion of constituent elements.

[0083] Unless the context requires otherwise, throughout the specification, "plurality" means "at least two", and "including" is interpreted as an open, inclusive meaning, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" are intended to indicate that a particular feature, structure, material or characteristic associated with the embodiment or example is included in at least one embodiment or example of the specification. The schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0084] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0085] Overview

[0086] As mentioned in the background technology, the process formula is very important in the process of process production. How to determine the appropriate process parameters to meet the process requirements has become an inevitable problem in the process of process generation. Figure 1 As shown, after the process requirements are input, a set of process parameters are usually randomly given to determine whether the current process parameters meet the requirements. If not, the process parameters need to be updated according to the degree of deviation from the expected results. Only after the requirements are met will the process production be executed according to the latest process parameters.

[0087] Although a neural network model is used to determine the process parameters, the principle of the model is still to use self-consistent iteration to slowly find the optimal process parameters. In order to shorten the time to determine the process parameters, the inventors have found through research that the required process parameters can be directly predicted based on the expected process results. At the same time, combined with the characteristics of the capsule network, the target network model can be used to quickly obtain the process parameters that meet the process requirements.

[0088] Based on the above concept, the method for determining the process parameters provided in the embodiment of this specification will be exemplarily described below.

[0089] Exemplary Methods

[0090] like Figure 2 As shown, the embodiment of this specification provides a method for determining process parameters, and the method for determining process parameters includes:

[0091] S201: In response to target process information input by a user, input the target process information into a target network model.

[0092] In this step, the target process information includes the process result that can be achieved by the process recipe to be determined. Among them, the process recipe to be determined is the process recipe expected to be obtained or the process recipe expected to meet the process requirements. The process result can be understood as the process requirement or the result of the process requirement. Specifically, in the process generation process, if it is necessary to determine a process recipe that can achieve a certain process result or meet a certain process requirement, the process recipe can be regarded as the process recipe to be determined, and the process result or process requirement can be regarded as the target process information. For example, for semiconductor process equipment, it is assumed that it is necessary to determine the specific parameter values ​​of the process temperature, process pressure and gas flow rate when the deposition rate reaches 10 nanometers per minute. The deposition rate reaches 10 nanometers per minute, which is the target process information, and the specific parameter values ​​of the process temperature, process pressure and gas flow rate are the process recipe to be determined or the process parameters in the process recipe. It can be understood that the target process information and the process recipe are related to the process production process involved in the process equipment, and the process production process includes but is not limited to the process production process of semiconductor process equipment.

[0093] It should be noted that the method provided in this specification can be directly applied to the process equipment to determine the process parameters. Therefore, after determining the process parameters, the process production can be directly carried out according to the process parameters. In this case, the user only needs to input the target process information into the process equipment. Of course, the process parameters can also be determined by the method provided in this embodiment with the help of other electronic devices with relatively strong computing power. Then the process parameters are input into the process equipment so that the process equipment performs process production according to the input process parameters.

[0094] S202: Predicting the process parameters required to achieve the process result through the target network model.

[0095] S203: Determine the prediction results output by the target network model as process parameters of the process recipe to be determined.

[0096] It should be noted that the target network model is a pre-trained network model, whose input is the process requirements or process results, and whose output is the predicted process parameters that can meet the process requirements or achieve the process results, that is, the predicted results. Therefore, with the help of the target network model, the process parameters corresponding to the target process information can be obtained, that is, the process parameters that can achieve the process results in the target process information can be obtained. After determining the process parameters, the process formula to be determined can be determined. Furthermore, the process equipment can use the determined process formula to be determined for process production.

[0097] The target network model includes: a capsule network layer built based on a capsule network. Among them, the capsule network is Capsule Net, which has more obvious advantages than the convolutional neural network in some aspects. For example, the capsule network can obtain richer object features through hierarchical feature extraction. In contrast, the convolutional neural network mainly extracts features through tiling and pooling operations, which may lose some detailed information. The capsule network represents objects in the form of vectors. This distributed representation can better capture the internal structure and relationship of the object. Convolutional neural networks generally use scalars to represent features, which makes it difficult to represent the complex structure of objects.

[0098] In this embodiment, for the process parameters of the process recipe to be determined, the process results that can be achieved or expected to be achieved, that is, the process requirements, are first determined. Then, the process parameters that can achieve this process result are predicted with the help of the target network model, and finally the predicted process parameters are used as the process parameters of the process recipe to be determined. Compared with the self-consistent iteration method to reversely find the optimal process parameters, the present application can quickly determine the process parameters that can meet the process requirements, saving a lot of time. At the same time, the target network model includes a capsule network layer constructed based on the capsule network, so that the target network model integrates the characteristics of the capsule network, and can better take into account the correlation between different features, thereby improving the accuracy of the process parameters.

[0099] In some embodiments, the capsule network layer includes: an input layer, a convolutional layer, a main capsule layer, a digital capsule layer, and an output layer;

[0100] The target network model predicts the process parameters required to achieve the process results, including:

[0101] Preprocess the target process information through the input layer to generate input features;

[0102] Extract input features through convolutional layers to generate initial feature vectors;

[0103] Extract features from the initial feature vector through the main capsule layer to generate an initial capsule vector;

[0104] Performing feature processing on the initial capsule vector through the digital capsule layer to generate a target number of result capsule vectors; wherein each result capsule vector corresponds to a process parameter, and the result capsule vector includes multiple predicted values ​​of the corresponding process parameter and the predicted probability of each predicted value;

[0105] Based on the result capsule vector, the output layer outputs the predicted value with the highest prediction probability for each process parameter.

[0106] It should be noted that the input of the input layer is the input of the target network model, which is used to preprocess the input data of the input target network model, so as to process it into features that can be directly recognized and processed. The input of the convolution layer is the output of the input layer, which is used to perform convolution processing on the output of the input layer to generate an initial feature vector. The convolution processing is not described in detail here. Optionally, the convolution layer in this embodiment can adopt a three-layer convolution structure to avoid the network structure being too complicated, and can be applied to large-scale equipment parameter adjustment. The input of the main capsule layer is the output of the convolution layer, which is used to process the output of the convolution layer to generate an initial capsule vector. Among them, the main capsule layer is the PrimaryCaps layer in the capsule network, which can realize the data processing of the PrimaryCaps layer, which is not described in detail here. The input of the digital capsule layer is the output of the main capsule layer, which is used to process the output of the main capsule layer to generate a result capsule vector. Among them, the digital capsule layer is the DigitCaps layer of the capsule network, which can realize the data processing of the DigitCaps layer, which is not described in detail here. The input of the output layer is the output of the digital capsule layer, which is used to process the output of the digital capsule layer to obtain the output of the final target network model. The output of the output layer is the output of the target network model.

[0107] Specifically, the input layer preprocesses the target process information, including: unifying the length of the target process information and performing IDX conversion. Among them, unifying the length includes: adjusting the input data to the same length, which can be achieved by filling null values ​​or truncating. IDX is a binary file format, usually used to store multi-dimensional array data. It is converted into a tensor or matrix format that can adapt to the network model for input through IDX.

[0108] The processing process of the convolution layer includes: using formula one to process the input features.

[0109] Formula 1: Conv i =σ(Wconv ·x i +b conv );

[0110] Among them, Conv i represents the initial eigenvector, W conv represents the convolution kernel, b conv represents the bias term, σ is a nonlinear activation function, such as RELU function, but not limited to this, x i Represents the input features.

[0111] It can be understood that the larger the convolution kernel, the greater the amount of information contained in the initial feature vector.

[0112] The processing process of the main capsule layer includes: processing the initial feature vector using formula 2.

[0113] Formula 2: Cap conv =ρ(W cap Conv i +b cap );

[0114] Among them, Cap conv represents the initial capsule vector, W cap Represents the convolution kernel of the main capsule layer, Conv i represents the initial eigenvector, b cap represents the bias term, and ρ is the squash function in the capsule network.

[0115] The main capsule layer can convert the multi-dimensional data output by the convolutional layer into several capsules or capsule vectors. Specifically, it performs deep feature extraction on the target process information.

[0116] The processing process of the digital capsule layer includes: processing the initial capsule vector using formula three.

[0117] Formula 3: Cap full =φ(W full ·Cap conv +b full );

[0118] Among them, Cap full Represents the result capsule vector, Cap conv represents the initial capsule vector, W full and b full are weights and biases respectively. φ represents the transformation matrix.

[0119] It can be understood that each capsule is associated with the previous capsule network through a transformation matrix, and the transformation matrix is ​​used to predict the input. Through the calculation of the digital capsule layer, multiple capsule vectors containing feature information, namely, result capsule vectors, can be obtained, and the number of capsule vectors is equal to the number of process parameters in the process recipe to be determined.

[0120] In this embodiment, a capsule network layer is composed of an input layer, a convolutional layer, a main capsule layer, a digital capsule layer and an output layer, which can well integrate the characteristics of the capsule network in the target network model while avoiding the network structure of the capsule network layer from being too complicated.

[0121] In some embodiments, extracting features from the initial feature vector through the main capsule layer to generate the initial capsule vector includes:

[0122] The initial feature vector is extracted through the main capsule layer in a dynamic routing manner to generate an initial capsule vector.

[0123] It should be noted that in the capsule network, the main capsule layer can be regarded as composed of multiple layers of capsules, and each capsule in each layer of capsules is connected to each capsule in the next layer. In addition, each capsule performs the same dynamic routing processing on the output of the previous layer of capsules, and finally, the output of the last layer of capsules is used as the initial capsule vector. In the process of the low-level capsule passing the input vector to the high-level capsule, the corresponding parameters are continuously adjusted so that the high-level capsule can output more appropriate data. This process can be called dynamic routing. Since the process of dynamic routing processing is the same for each capsule, only one capsule is used as an example here. Figure 3 As shown in , assume that the previous layer of capsules includes two capsules, and the outputs of the two capsules are V1 and V2, and the weights are W1 and W2. Then, the coupling coefficient used by the capsule in the first processing is and The result is S1. After processing with the extrusion function, the coupling coefficient used in the update process is obtained and A second pass is then performed in this capsule, using the updated coupling coefficients and The new result S2 is obtained. After being processed by the extrusion function, the coupling coefficient used in the processing is updated to obtain and Then a third pass is performed in this capsule and the updated coupling coefficients are used and The new result S3 is obtained. When S3 is determined to be appropriate data, the real output V of the capsule is obtained through the extrusion function. It can be understood that when the probability of occurrence of process parameters is expressed based on the length of the capsule, the module length of the capsule can indicate the significance of the feature. The longer the module length, the more significant the feature. At the same time, when the module length is close to 0, it also has an amplification effect, and there is no need to use the extrusion function for global compression. Among them, the extrusion function is as follows:

[0124]

[0125] Among them, the squash function squash(v j ) is mainly divided into two parts. The first part changes the input vector s by squeezing function j length, does not change the input vector s j The second part controls the size of the first part through a vector of length 1. This preserves the input vector s j The direction of the vector can also be controlled. j , which can be determined by the following formula:

[0126]

[0127] Among them, u i represents the output of the lower-level capsule, W ij represents the weight parameter, c ij Represents the coupling coefficient, where there is a coupling coefficient between the high-level capsule and the low-level capsule, and the sum of the coupling coefficients is 1.

[0128] In the process of updating the coupling coefficient, the intermediate parameter b can be used. The details are as follows:

[0129] in,

[0130]

[0131] Among them, b ij Indicates the intermediate parameter, u i represents the output of the lower-level capsule, v j Represents the output of the high-level capsule. and v j When they are in the same direction, it means that the features expressed by the lower-level capsules and the higher-level capsules are more similar. ij The weight of will increase; on the contrary, the low-level capsule feature is highly likely not to belong to the high-level capsule, b ij The dynamic routing algorithm establishes the connection between the low-level capsule and the high-level capsule by updating the weight.

[0132] Of course, in addition to dynamic routing, clustering algorithms can also be used to generate initial capsule vectors. It can be understood that when using clustering algorithms, the process from low-level capsules to high-level capsules is similar to the process of generating cluster centroids in the clustering process. Assume that the characteristics of the low-level capsules are u 1 ,u 2 ,…u n , and it is divided into k categories. The process of determining the initial capsule vector can be understood as finding the central feature v of k high-level capsules 1 ,v 2 ,…,v k , and make the intra-class interval minimum. The intra-class interval can be calculated based on formula 4.

[0133] Formula 4:

[0134] Among them, L represents the minimum interval within the class, u i represents the output of the lower-level capsule, v j Represents the output of the high-level capsule. d represents the distance measure between the low-level capsule and the high-level capsule. The clustering result depends on the specific form of d.

[0135] Add the distances between the categories of all high-level capsules, and the goal is to minimize the distance, then Among them, v 1 ,v 2 ,…,v k represents the central feature of k high-level capsules, and L represents the minimum interval within the class. Then L can be solved with the help of the approximate value formula. Specifically,

[0136] Approximate formula 1:

[0137] Approximate formula 2: min(λ 1 ,λ 2 ,...λ n )=-max(-λ 1 ,-λ 2 ,...-λ n );

[0138] Among them, λ 1 , 2 ... n Represents n values.

[0139] Based on the above approximate formula, the calculation formula for the minimum interval within the class can be obtained:

[0140]

[0141] After mathematical calculation, we can get:

[0142]

[0143] make Indicates v j The result of the rth iteration of , <,> represents the scalar product operation, c ij represents the coupling coefficient, u i represents the output of the lower-level capsule, v i Represents the output of the high-level capsule, and the relationship between the low-level capsule and the high-level capsule can be obtained. That is:

[0144] in, represents the coupling coefficient of the rth iteration, u i represents the output of the lower-level capsule, Represents the output of the high-level capsule at the r+1th iteration.

[0145] Among them, the transformation matrix W is introduced ij The dimensions of the lower-level capsules and the higher-level capsule centers can be changed arbitrarily to ensure the feature expression capability of the higher-level capsules. Accordingly, the relationship between the lower-level capsules and the higher-level capsules will change to:

[0146] Among them, W ij represents the transformation matrix, represents the coupling coefficient of the rth iteration, u i represents the output of the lower-level capsule, Represents the output of the high-level capsule at the r+1th iteration.

[0147] In this embodiment, by means of the characteristics of dynamic routing, the connection between various parameters can be strengthened, so that the internal structure and relationship of the processing object can be better captured. Compared with other clustering algorithms, the network structure in this embodiment is more lightweight.

[0148] To adapt to process parameters in different execution chambers, in some implementation methods, before performing feature processing on the initial capsule vector through the digital capsule layer to generate a target number of result capsule vectors, the method further includes:

[0149] Determining an execution chamber based on target process information through a target network model, wherein the execution chamber is a chamber in a process equipment that executes a process recipe to be determined;

[0150] Based on the correspondence between each chamber in the process equipment and the number of process parameters, the number of process parameters corresponding to the execution chamber is determined as the target number through the target network model.

[0151] It should be noted that the process equipment includes multiple execution chambers, and different execution chambers can perform different process production according to different process parameters or process recipes. For example, semiconductor process equipment includes: MOW chamber, CVDW chamber, wherein the process parameters executed by the MOW chamber include: process temperature, process pressure, gas flow rate, etc. The process parameters executed by the CVDW chamber include: Soak time, process temperature, etc.

[0152] The target network model can be applied to multiple execution chambers. Since the process results of different execution chambers are different, different execution chambers can be distinguished by the process results, so as to determine which process parameters need to be output. Of course, the target network model can also be applied to only one execution chamber. Regardless of which execution chamber's target process information is input to the target network model, its output is the process parameters of a fixed execution chamber.

[0153] In this embodiment, the target network model can determine the execution chamber based on the target process information, thereby predicting the process parameters for the determined execution chamber, so that the target network model can adapt to the process parameters in different execution chambers.

[0154] To further improve the accuracy of the model output and avoid the problem of gradient disappearance or gradient explosion, in some implementation methods, the target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, wherein the input of the long short-term memory network layer is the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

[0155] It should be noted that the long short-term memory network is an LSTM (Long Short-Term Memory) network. The long short-term memory network layer in the target network model integrates the advantages of the LSTM network in terms of gradient, which can avoid the problems of gradient disappearance and gradient explosion. In this embodiment, the long short-term memory network layer is located behind the capsule network layer, and the input of the long short-term memory network layer is the output of the capsule network layer. The output of the long short-term memory network layer is the output of the target network model.

[0156] In this embodiment, the advantages of the LSTM network in terms of gradient are integrated. By setting a long short-term memory network layer in the target network model, not only the problems of gradient vanishing and gradient explosion can be avoided; at the same time, the timing characteristics of the target process information can be considered to improve the accuracy of the model output.

[0157] In some embodiments, predicting the process parameters required to achieve the process result by the target network model includes:

[0158] The target process information is processed through the capsule network layer to generate a target number of result capsule vectors; wherein each result capsule vector corresponds to a process parameter, and the result capsule vector includes multiple predicted values ​​of the corresponding process parameter and the predicted probability of each predicted value;

[0159] The result capsule vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process results.

[0160] It should be noted that the network architecture of the long short-term memory network layer is as follows Figure 4 As shown. Among them, the forget gate is f t , the input gate is i t , the output gate is o t . Input gate i t The input is composed of the historical state h t-1 and the current node state x t σ is the sigmoid function, which controls the output between 0 and 1. 1 means retaining historical information, and 0 means forgetting historical information. The input gate can be calculated by the following formula:

[0161] i t =σ(W ix ·x t +W ih ·h t-1 +b i );

[0162] Among them, i t Represents the output result of the input gate, W ix Represents the weight matrix of the current state at the input gate, W ih represents the weight matrix of the historical state at the input gate, b i Represents the weight bias for the input gate.

[0163] Similarly, the forget gate and output gate can be calculated by the following formula:

[0164] f t =σ(W fx ·x t +W fh ·h t-1 +b f );o t =σ(W ox ·x t +W oh ·h t-1 +b o );

[0165] Among them, f t Represents the output of the forget gate, Wfx Represents the weight matrix of the current state in the forget gate, W fh Represents the weight matrix of the historical state in the forget gate, b f Represents the weight bias of the forget gate; o t Represents the output result of the output gate, W ox Represents the weight matrix of the current state at the output gate, W oh Represents the weight matrix of the historical state at the output gate, b o Represents the weight bias for the output gate.

[0166] Then, according to the previous state g t and the current state s t Select new information to pass to the next neuron. The specific process is as follows:

[0167] g t =σ(W ox ·x t +W oh ·h t-1 +b o );s t =g t ·i t +s t-1 ·f t ;h t =tanh(s t )·o t ; Use the tanh function to process the current state and multiply it with the output gate to achieve the hidden vector h t , and then connect with the next unit to extract the temporal feature information of the previous and next data packets during the transmission process. Finally, the softmax function is used to obtain the final prediction output result.

[0168] In this embodiment, a long short-term memory network layer is formed by a forget gate, an input gate, an output gate, and an output layer to process the result capsule vector, taking the timing characteristics of the data into consideration, so that more accurate process parameters can be obtained.

[0169] In order to further improve the accuracy of the model output and avoid the problem of gradient disappearance or gradient explosion, in some implementation methods, the target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, and an attention module constructed based on an attention mechanism; wherein the input of the attention module is the target process information, the input of the long short-term memory network layer is the fusion result of the output of the attention module and the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

[0170] It should be noted that the situation of introducing LSTM network in the target network model is similar to the situation of introducing LSTM network in the above embodiment, which will not be repeated here. Among them, the difference between the two is that the present application also introduces an attention module built based on the attention mechanism. Among them, the attention mechanism can be understood as a method of imitating human visual and cognitive systems, allowing the neural network model to focus on the relevant parts when processing input data. By introducing the attention mechanism, the neural network model can automatically learn and selectively pay attention to the important information in the input, thereby improving the performance and generalization ability of the model. After the attention module is trained by the model, its output can indicate the importance of each feature output by the capsule network layer. Then the output of the attention module and the output of the capsule network layer are fused, which can increase the proportion of features with higher importance, thereby focusing on important information. Then the fusion result is input into the LSTM network, and more accurate results can be obtained by continuously adjusting the weights between the features.

[0171] In this embodiment, the advantages of the LSTM network in terms of gradient are integrated. By setting a long short-term memory network layer in the target network model, not only the problems of gradient vanishing and gradient explosion can be avoided; at the same time, the timing characteristics of the target process information can be considered, and the attention mechanism is introduced to further improve the accuracy of the model output.

[0172] In some embodiments, predicting the process parameters required to achieve the process result by the target network model includes:

[0173] The target process information is processed through the capsule network layer to generate a target number of result capsule vectors; wherein each result capsule vector corresponds to a process parameter, and the result capsule vector includes multiple predicted values ​​of the corresponding process parameter and the predicted probability of each predicted value;

[0174] The target process information is processed through the attention module to generate a context vector corresponding to each result capsule vector;

[0175] The fusion result of the result capsule vector and the context vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process results.

[0176] It should be noted that the network architecture of the long short-term memory network layer is as follows Figure 4As shown. The situation in which the long short-term memory network layer in the target network model processes the fusion result is similar to the situation in which the long short-term memory network layer processes the result capsule vector in the above embodiment, and will not be repeated here. Among them, the objects processed by this application are different, that is, the fusion result of the result capsule vector and the context vector. The attention mechanism will not be described in detail here. It can be understood that the context vector is the output of the attention module. The context vector corresponding to each result capsule vector, the numerical value of the context vector is positively correlated with the importance of the result capsule vector. In other words, the higher the importance of a certain result capsule vector in the process of predicting process parameters, the larger the corresponding context vector. This is the ability learned by the attention module based on the attention mechanism during the model training process. Specifically, multiple partially overlapping continuous subsequences can be intercepted from the original input sequence, that is, the target process information, in the input layer as the input of the attention module. The context vector output by the attention module can be regarded as a significant feature, which can identify the importance of each output feature of the digital capsule layer. The higher the importance of the output feature of the digital capsule layer, the closer the output of the corresponding attention module is to 1; conversely, the lower the importance of the output feature of the digital capsule layer, the closer the output value of the corresponding attention module is to 0. Then, the output features of the digital capsule layer are fused with the salient features of the output of the corresponding attention module. The fusion method is to multiply the output features of the digital capsule layer and the salient features of the output of the corresponding attention module element by element. Since the value of the output of the attention module reflects the importance of the output features of the digital capsule layer, the identification of important features is completed, which is more conducive to the processing of the subsequent LSTM network layer. Therefore, the accuracy of the model can be further improved. In the process of model training, the introduction of the attention mechanism can focus on important information with high weight and continuously adjust the weights between features, so that the accuracy of the prediction results is improved compared with the accuracy of the existing algorithm.

[0177] In this embodiment, a long short-term memory network layer is formed by a forget gate, an input gate, an output gate, and an output layer to process the fusion result, taking into account the temporal characteristics of the data, and obtaining more accurate process parameters. At the same time, the introduction of the attention mechanism can reduce the interference of useless parameters, solve the problem of information overload, and weight the data that has a greater impact on the results among a large number of input information, which can improve the accuracy of the final prediction.

[0178] In some implementation methods, in response to the target process information input by the user, before inputting the target process information into the target network model, the method further includes:

[0179] Acquire training data; wherein the training data includes: historical process formulas and process results that can be achieved by the historical process formulas;

[0180] The network model constructed by the capsule network is trained using the training data, and the trained network model is determined as the target network model.

[0181] It should be noted that the historical process formula is the process formula used in the historical process generation process. The corresponding process result is the process result that can be achieved. For example, in the historical process generation process, when the historical process formula A is used, the process result a can be achieved. When the historical process formula B is used, the process result b can be achieved. Then both the historical process formula A and the historical process formula B can be used as training data.

[0182] For the training of the network model constructed by the capsule network, the training process is the same as that of the neural network model. Both use pre-prepared training data for training, so that the trained network model can accurately predict the process parameters based on the process results. In some embodiments, the data set required for training can be prepared in advance, and then the data set is divided into 80% offline training data and 20% real-time test data. To achieve model training and testing. During the test, the parameters are adjusted by the accuracy of the prediction results. If the loss function and the gradient descent function are overfitted, the network architecture and parameters are continued to be adjusted to improve the generalization ability, and the predicted process results are obtained through the final adjusted model.

[0183] In this embodiment, the training method of the neural network model can be used to implement model training of the network model constructed by the capsule network.

[0184] In some embodiments, during the model training process of the network model constructed by the capsule network using the training data, the sum of the interval loss and the reconstruction loss is used as the model loss.

[0185] It should be noted that the separation loss can be determined as the sum of K types of separation losses. Specifically,

[0186] in, L margin Indicates the interval loss. T k Represents the loss value of the k-th prediction result. If the prediction result is consistent with the label, T k is 1, otherwise it is 0. Usually λ is 0.5, m + =0.9, m - =0.9, v k Represents the output of the k-th high-level capsule.

[0187] For the reconstruction loss, the reconstruction loss can be obtained by calculating the Euclidean distance between the prediction and the input. Therefore, in some implementations, when the intermediate interval loss is taken as the dominant factor, the model loss is as follows:

[0188] L=Lmargin +0.005*L reconstruct , where L represents the model loss, L margin Indicates the interval loss, L reconstruct represents the reconstruction loss.

[0189] In this embodiment, the sum of the margin loss and the reconstruction loss is used as the model loss, which can have the advantages of both loss functions and prevent overfitting.

[0190] In another embodiment of the present specification, offline data is used to continuously adjust and optimize the model during the training process, and the modified model is synchronized to the online in real time to process the real-time data. Then, the data generated during the real-time data processing is used as offline training data to expand the training library, thereby improving the prediction accuracy of the model. Specifically, Figure 5 As shown in the figure, it includes the offline data processing stage and the real-time data processing stage. The offline data processing stage includes: offline data training, adjusting the gradient optimization method and the objective function, model parameter tuning, outputting the accuracy and analyzing it, and modifying the model according to the analysis. In this way, the optimized model can be obtained and updated to the real-time data processing stage. Real-time data processing stage: real-time input data, predicting the real-time data, outputting the accuracy and analyzing it, and optimizing the model according to the results. At the same time, the generated data is synchronized to the offline training data to expand the training library.

[0191] The following is a specific example to illustrate the method for determining the process parameters provided in this application. Figure 6 As shown, the method is applied to process equipment, specifically comprising:

[0192] Step S601: inputting original process requirement data into the process equipment, wherein the original process requirement data is equivalent to the target process information in the aforementioned embodiment, which will not be described in detail here.

[0193] Step S602: Data processing: This is equivalent to the input layer of the capsule network layer, and pre-processes the original process requirement data.

[0194] Step S603: The processed data is predicted using the capsule long short-term memory network model to obtain process parameters that meet the original process requirement data.

[0195] Step S604: Determine the process parameters output by the capsule long short-term memory network model as process parameters that can meet the original process requirement data.

[0196] It can be understood that the application architecture of this embodiment is as follows Figure 7As shown in FIG. , it includes: input layer, convolution layer, main capsule layer, digital capsule layer, long short-term memory network layer, and output layer. Of course, the application architecture of this embodiment can also be as follows: Figure 8 As shown, it includes: an input layer, a convolution layer, a main capsule layer, a digital capsule layer, an attention module, a long short-term memory network layer, and an output layer. Among them, the input layer is used to execute step S602. The convolution layer, the main capsule layer, the digital capsule layer, the attention module and the long short-term memory network layer can refer to the convolution layer, the main capsule layer, the digital capsule layer, the attention module and the long short-term memory network layer in the aforementioned embodiments. The output layer is composed of a softmax function. For the introduction of each part in the application architecture, please refer to the description of the corresponding parts in the aforementioned embodiments, which will not be described here.

[0197] In this embodiment, the efficiency of obtaining process parameters is improved. Process personnel do not need to manually try or guess the approximate range of process parameters. At the same time, it can adapt to different inputs of different chambers to obtain different outputs, and is widely used. In addition, the capsule long short-term memory network can obtain the characteristics of the process results in all directions through a unique dynamic routing algorithm, making the prediction results more accurate.

[0198] Exemplary Devices

[0199] Some embodiments of this specification also provide a device for determining process parameters, such as Fig. 9 As shown, the device for determining the process parameters includes:

[0200] A response module 91 is used to respond to the target process information input by the user and input the target process information into the target network model; wherein the target process information includes the process result that can be achieved by the process recipe to be determined;

[0201] The model processing module 92 is used to predict the process parameters required to achieve the process result through the target network model; wherein the target network model includes: a capsule network layer constructed based on the capsule network;

[0202] The determination module 93 is used to determine the prediction results output by the target network model as the process parameters of the process recipe to be determined.

[0203] In some embodiments, the capsule network layer includes: an input layer, a convolutional layer, a main capsule layer, a digital capsule layer, and an output layer;

[0204] The model processing module 92 includes:

[0205] A preprocessing unit, used for preprocessing target process information through an input layer to generate input features;

[0206] A first feature extraction unit, used to extract input features through a convolution layer to generate an initial feature vector;

[0207] A second feature extraction unit is used to extract features from the initial feature vector through a main capsule layer to generate an initial capsule vector;

[0208] A model processing unit, configured to perform feature processing on the initial capsule vector through a digital capsule layer to generate a target number of result capsule vectors; wherein each result capsule vector corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each predicted value;

[0209] The model output unit is used to output the predicted value with the maximum predicted probability of each process parameter based on the result capsule vector through the output layer.

[0210] In some embodiments, the second feature extraction unit is specifically configured to extract features from the initial feature vector in a dynamic routing manner through the main capsule layer to generate an initial capsule vector.

[0211] In some embodiments, the device further comprises:

[0212] A chamber determination module, used to determine an execution chamber based on target process information through a target network model, wherein the execution chamber is a chamber in a process equipment that executes a process recipe to be determined;

[0213] The quantity determination module is used to determine the quantity of process parameters corresponding to the execution chamber as the target quantity based on the correspondence between each chamber in the process equipment and the quantity of process parameters through the target network model.

[0214] In some embodiments, the target network model also includes: a long short-term memory network layer constructed based on the long short-term memory network, wherein the input of the long short-term memory network layer is the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

[0215] In some implementations, the model processing module 92 is specifically configured to:

[0216] The target process information is processed through the capsule network layer to generate a target number of result capsule vectors; wherein each result capsule vector corresponds to a process parameter, and the result capsule vector includes multiple predicted values ​​of the corresponding process parameter and the predicted probability of each predicted value;

[0217] The result capsule vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process results.

[0218] In some embodiments, the target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, and an attention module constructed based on an attention mechanism; wherein the input of the attention module is the target process information, the input of the long short-term memory network layer is the fusion result of the output of the attention module and the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

[0219] In some implementations, the model processing module 92 is specifically configured to:

[0220] The target process information is processed through the capsule network layer to generate a target number of result capsule vectors; wherein each result capsule vector corresponds to a process parameter, and the result capsule vector includes multiple predicted values ​​of the corresponding process parameter and the predicted probability of each predicted value;

[0221] The target process information is processed through the attention module to generate a context vector corresponding to each result capsule vector;

[0222] The fusion result of the result capsule vector and the context vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process results.

[0223] In some embodiments, the device further comprises:

[0224] Model training module, used to:

[0225] Acquire training data; wherein the training data includes: historical process formulas and process results that can be achieved by the historical process formulas;

[0226] The network model constructed by the capsule network is trained using the training data, and the trained network model is determined as the target network model.

[0227] In some embodiments, during the model training process of the network model constructed by the capsule network using the training data, the sum of the interval loss and the reconstruction loss is used as the model loss.

[0228] The device for determining process parameters provided in the embodiment of this specification belongs to the same inventive concept as the method for determining process parameters provided in the above embodiment of this specification. For technical details not fully described in this embodiment, please refer to the specific processing content of the method for determining process parameters provided in the above embodiment of this specification, and will not be repeated here.

[0229] Exemplary semiconductor process equipment and electronic equipment

[0230] Another embodiment of the present application further provides a semiconductor process equipment, comprising: a host computer and a slave computer connected to the host computer;

[0231] The upper computer is used to execute the method for determining the process parameters of various embodiments of this specification to determine the process parameters of the process recipe to be determined, and send the process parameters of the process recipe to be determined to the lower computer;

[0232] The lower computer is used to receive the process parameters of the process recipe to be determined, and perform process production according to the process parameters of the process recipe to be determined.

[0233] Another embodiment of the present application also provides an electronic device, see Fig.10 As shown, an exemplary embodiment of the present specification also provides an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for determining process parameters according to various embodiments of the present specification described in the above embodiments of the present specification are executed.

[0234] The internal structure of the electronic device can be as follows Fig.10 As shown, the electronic device includes a processor, a memory, a network interface and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the method for determining the process parameters according to various embodiments of the present specification described in the above embodiments of the present specification are performed.

[0235] The processor may include a main processor and may also include a baseband chip, a modem, etc.

[0236] The memory stores a computer program for executing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the computer program may include a program code, and the program code includes computer operation instructions. More specifically, the memory may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk storage, a flash, and the like.

[0237] The processor may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the scheme of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0238] The input device may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

[0239] Output devices may include means that allow information to be output to a user, such as display screens, printers, speakers, etc.

[0240] The communication interface may include using any transceiver or the like to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0241] The processor executes the computer program stored in the memory and calls other devices, which can be used to implement each step of the method for determining any process parameter provided in the above embodiments of the present application.

[0242] The electronic device may also include a display component and a voice component. The display component may be a liquid crystal display or an electronic ink display. The input device of the electronic device may be a touch layer covered on the display component, or a button, trackball or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad or mouse.

[0243] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of this specification, and does not constitute a limitation on the electronic device to which the scheme of this specification is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0244] Exemplary computer program products and computer-readable storage media

[0245] In addition to the above-mentioned methods and devices, the method for determining process parameters provided in the embodiments of this specification may also be a computer program product, which includes a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for determining process parameters according to various embodiments of this specification described in the above-mentioned "Exemplary Method" section of this specification.

[0246] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present specification, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0247] In addition, an embodiment of the present specification also provides a computer-readable storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the steps of the method for determining process parameters according to various embodiments of the present specification described in the above "Exemplary Method" section of the present specification.

[0248] It should be understood that the specific examples in this article are only intended to help those skilled in the art better understand the implementation methods of this specification, rather than to limit the scope of this specification.

[0249] It can be understood that in the various implementations of this specification, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of this specification.

[0250] It can be understood that the various embodiments described in this specification can be implemented individually or in combination, and the embodiments of this specification are not limited to this.

[0251] Unless otherwise specified, all technical and scientific terms used in the embodiments of this specification have the same meaning as those generally understood by those skilled in the art of the technical field of this specification. The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the scope of this specification. The term "and / or" used in this specification includes any and all combinations of one or more related listed items. The singular forms of "a", "above", and "the" used in the embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0252] It can be understood that the processor of the embodiment of this specification can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method implementation can be completed by the hardware integrated logic circuit or software instructions in the processor. The above processor can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiment of this specification can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of this specification can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor are combined and executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0253] It is understood that the memory in the embodiments of this specification may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (Programmable ROM, PROM), an erasable programmable read-only memory (ErasablePROM, EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory may be a random access memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0254] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this specification.

[0255] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0256] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0257] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0258] In addition, each functional unit in each embodiment of the present specification may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0259] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this specification, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of this specification. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0260] The above is only a specific implementation of this specification, but the protection scope of this specification is not limited to this. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this specification, which should be included in the protection scope of this specification. Therefore, the protection scope of this specification should be based on the protection scope of the claims.

Claims

1. A method for determining process parameters, characterized in that: The method comprises: In response to target process information input by a user, the target process information is input into a target network model; wherein the target process information includes a process result that can be achieved by the process recipe to be determined; Predicting the process parameters required to achieve the process result through the target network model; wherein the target network model includes: a capsule network layer constructed based on a capsule network; The prediction results output by the target network model are determined as the process parameters of the process recipe to be determined.

2. The method according to claim 1, characterized in that The capsule network layer includes: an input layer, a convolutional layer, a main capsule layer, a digital capsule layer and an output layer; Predicting the process parameters required to achieve the process result through the target network model includes: Preprocessing the target process information through the input layer to generate input features; Extracting features from the input features through the convolution layer to generate an initial feature vector; Extracting features from the initial feature vector through the main capsule layer to generate an initial capsule vector; Performing feature processing on the initial capsule vector through the digital capsule layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values; The output layer outputs a predicted value with the maximum predicted probability for each process parameter based on the result capsule vector.

3. The method according to claim 2, characterized in that Extracting features from the initial feature vector through the main capsule layer to generate an initial capsule vector includes: The initial feature vector is extracted by the main capsule layer in a dynamic routing manner to generate an initial capsule vector.

4. The method according to claim 2, characterized in that: Before performing feature processing on the initial capsule vector through the digital capsule layer to generate a target number of result capsule vectors, the method further includes: Determining an execution chamber based on the target process information through the target network model, wherein the execution chamber is a chamber in a process equipment that executes the process recipe to be determined; The target network model is used to determine the number of process parameters corresponding to the execution chamber as the target number based on the correspondence between each chamber in the process equipment and the number of process parameters.

5. The method according to claim 1, characterized in that The target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, wherein the input of the long short-term memory network layer is the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

6. The method according to claim 5, characterized in that Predicting the process parameters required to achieve the process result through the target network model includes: Processing the target process information through the capsule network layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values; The result capsule vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process result.

7. The method according to claim 1, characterized in that The target network model also includes: a long short-term memory network layer constructed based on a long short-term memory network, and an attention module constructed based on an attention mechanism; wherein the input of the attention module is the target process information, the input of the long short-term memory network layer is a fusion result of the output of the attention module and the output of the capsule network layer, and the output of the long short-term memory network layer is the output of the target network model.

8. The method according to claim 7, characterized in that Predicting the process parameters required to achieve the process result through the target network model includes: Processing the target process information through the capsule network layer to generate a target number of result capsule vectors; wherein each of the result capsule vectors corresponds to a process parameter, and the result capsule vector includes a plurality of predicted values ​​of the corresponding process parameter and a predicted probability of each of the predicted values; Processing the target process information through the attention module to generate a context vector corresponding to each of the result capsule vectors; The fusion result of the result capsule vector and the context vector is processed through the forget gate, input gate, output gate and output layer of the long short-term memory network layer to obtain the process parameters required to achieve the process result.

9. The method according to claim 1, characterized in that: In response to the target process information input by the user, before inputting the target process information into the target network model, the method further includes: Acquire training data; wherein the training data includes: historical process recipes and process results that can be achieved by the historical process recipes; The training data is used to perform model training on the network model constructed by the capsule network, and the trained network model is determined as the target network model.

10. The method according to claim 9, characterized in that In the process of training the network model constructed by the capsule network using the training data, the sum of the interval loss and the reconstruction loss is used as the model loss.

11. A device for determining process parameters, characterized in that: The device comprises: A response module, configured to respond to target process information input by a user and input the target process information into a target network model; wherein the target process information includes a process result that can be achieved by the process recipe to be determined; A model processing module, used to predict the process parameters required to achieve the process result through the target network model; wherein the target network model includes: a capsule network layer constructed based on a capsule network; The determination module is used to determine the prediction results output by the target network model as the process parameters of the process recipe to be determined.

12. A semiconductor process equipment, characterized in that: It includes: a host computer and a slave computer connected to the host computer; The upper computer is used to execute the method for determining the process parameters according to any one of claims 1 to 10 to determine the process parameters of the process recipe to be determined, and send the process parameters of the process recipe to be determined to the lower computer; The lower computer is used to receive the process parameters of the process recipe to be determined, and perform process production according to the process parameters of the process recipe to be determined.

13. An electronic device, characterized in that: include: Processor and memory; Wherein, the memory is connected to the processor, and the memory is used to store a computer program; The processor is used to implement the method for determining the process parameters according to any one of claims 1 to 10 by running the computer program stored in the memory.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for determining the process parameters according to any one of claims 1 to 10 is implemented.

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