CN114998330B - Unsupervised wafer defect detection method, unsupervised wafer defect detection device, unsupervised wafer defect detection equipment and storage medium - Google Patents
Unsupervised wafer defect detection method, unsupervised wafer defect detection device, unsupervised wafer defect detection equipment and storage medium Download PDFInfo
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Abstract
The invention belongs to the technical field of wafer detection, and discloses an unsupervised wafer defect detection method, device, equipment and storage medium. The method comprises the following steps: acquiring an initial wafer detection data set; utilizing a preset unsupervised self-encoder to make a pseudo label for the label-free wafer detection data in the initial wafer detection data set, and generating a target wafer detection data set; training a preset classifier according to the target wafer detection data set to obtain a trained target classifier; and when the wafer data to be detected is received, detecting the wafer data to be detected by using the target classifier to obtain a wafer defect detection result. By the mode, the pseudo labels are made for the label-free wafer detection data, the label data and the pseudo label data are used for training the target classifier, so that the target classifier learns different defect characteristics, the unknown defect types can be identified, and the detection precision of wafer defects is improved.
Description
Technical Field
The invention relates to the technical field of wafer detection, in particular to an unsupervised wafer defect detection method, device, equipment and storage medium.
Background
In the Micro LED display technology, multiple fault types are generated in the wafer (wafer) production process due to multiple influences such as differences in preparation equipment, production process, material design, and the like, and there is no coupling between the fault types. Wafer detection data is large in amount and free of labels, and wafer detection data is large in amount and cannot be obtained easily. In the existing wafer detection method, a relatively stable detection model can be trained only by using a large amount of label data, but the acquired label-free data is difficult to use, and only a few defect types can be learned, so that the detection accuracy is not high in unknown defect types.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide an unsupervised wafer defect detection method, device, equipment and storage medium, and aims to solve the technical problems that a relatively stable detection model can be trained only by using a large amount of label data in the conventional wafer detection method, the acquired label-free data is difficult to use, and only a few defect types can be learned, so that the detection precision is not high in the unknown defect types.
In order to achieve the above object, the present invention provides an unsupervised wafer defect detecting method, which comprises the following steps:
acquiring an initial wafer detection data set;
utilizing a preset unsupervised self-encoder to make a pseudo label for the label-free wafer detection data in the initial wafer detection data set, and generating a target wafer detection data set;
training a preset classifier according to the target wafer detection data set to obtain a trained target classifier;
and when receiving the data of the wafer to be detected, detecting the data of the wafer to be detected by using the target classifier to obtain a wafer defect detection result.
Optionally, the preset unsupervised self-encoder and the preset arbiter form a countermeasure self-encoding network;
the method for generating the target wafer detection data set by using the pseudo label made by the preset unsupervised self-encoder for the label-free wafer detection data in the initial wafer detection data set comprises the following steps:
inputting unlabeled wafer inspection data in the initial wafer inspection data set to the countering self-coding network;
predicting the label of the label-free wafer detection data by using a preset unsupervised self-encoder in the antagonistic self-encoding network, and constraining the prediction result of the preset unsupervised self-encoder by using a preset discriminator in the antagonistic self-encoding network to obtain a pseudo label corresponding to the label-free wafer detection data;
and generating a target wafer detection data set according to the pseudo label and the initial wafer detection data set.
Optionally, before the preset unsupervised auto-encoder is used to make a pseudo tag for the unlabeled wafer inspection data in the initial wafer inspection dataset and generate the target wafer inspection dataset, the method further includes:
and performing preliminary training on an initial discriminator according to the labeled wafer detection data in the initial wafer detection data set to obtain a preset discriminator.
Optionally, the preset unsupervised auto-encoder is formed by a stack of multiple auto-encoders, wherein the multiple auto-encoders are provided with different activation functions.
Optionally, the generating a target wafer inspection dataset by using a preset unsupervised auto-encoder to make a pseudo tag for the unlabeled wafer inspection data in the initial wafer inspection dataset includes:
predicting labels of the label-free wafer detection data in the initial wafer detection data set by using a plurality of self-encoders to obtain a plurality of prediction results;
and determining a pseudo label corresponding to the label-free wafer detection data according to the plurality of prediction results to generate a target wafer detection data set.
Optionally, after the labels of the unlabeled wafer inspection data in the initial wafer inspection dataset are respectively predicted by using a plurality of self-encoders to obtain a plurality of prediction results, the method further includes:
determining prediction accuracies respectively corresponding to the plurality of self-encoders;
deleting the self-encoders with the prediction accuracy lower than a preset threshold value from the self-encoders to obtain a plurality of residual self-encoders;
and determining a pseudo label corresponding to the label-free wafer detection data according to the prediction results corresponding to the residual self-encoders to generate a target wafer detection data set.
Optionally, the determining, according to the prediction results corresponding to the multiple remaining self-encoders, the pseudo label corresponding to the unlabeled wafer inspection data to generate a target wafer inspection data set includes:
determining a weight value corresponding to each residual self-encoder according to the prediction accuracy corresponding to the residual self-encoders;
and determining a pseudo label corresponding to the label-free wafer detection data according to the prediction results corresponding to the residual self-encoders and the weight values, and generating a target wafer detection data set.
In addition, in order to achieve the above object, the present invention further provides an unsupervised wafer defect detecting apparatus, including:
the acquisition module is used for acquiring an initial wafer detection data set;
the label generation module is used for making a pseudo label for the label-free wafer detection data in the initial wafer detection data set by using a preset unsupervised self-encoder to generate a target wafer detection data set;
the training module is used for training a preset classifier according to the target wafer detection data set to obtain a trained target classifier;
and the detection module is used for detecting the wafer data to be detected by using the target classifier when receiving the wafer data to be detected to obtain a wafer defect detection result.
In addition, in order to achieve the above object, the present invention further provides an unsupervised wafer defect detecting apparatus, including: a memory, a processor, and an unsupervised wafer defect detection program stored on the memory and executable on the processor, the unsupervised wafer defect detection program configured to implement the unsupervised wafer defect detection method as described above.
In addition, to achieve the above object, the present invention further provides a storage medium having an unsupervised wafer defect detecting program stored thereon, wherein the unsupervised wafer defect detecting program, when executed by a processor, implements the unsupervised wafer defect detecting method as described above.
The method comprises the steps of obtaining an initial wafer detection data set; utilizing a preset unsupervised self-encoder to make a pseudo label for the label-free wafer detection data in the initial wafer detection data set, and generating a target wafer detection data set; training a preset classifier according to the target wafer detection data set to obtain a trained target classifier; and when the wafer data to be detected is received, detecting the wafer data to be detected by using the target classifier to obtain a wafer defect detection result. By the mode, the pseudo labels are made for the label-free wafer detection data, the label data and the pseudo label data are used for training the target classifier, so that the target classifier learns different defect characteristics, the unknown defect types can be identified, and the detection precision of wafer defects is improved.
Drawings
FIG. 1 is a schematic structural diagram of an unsupervised wafer defect inspection apparatus for a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for unsupervised wafer defect inspection according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a second embodiment of an unsupervised wafer defect inspection method according to the present invention;
FIG. 4 is a schematic diagram of a model layout of an unsupervised wafer defect inspection method according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a method for unsupervised wafer defect inspection according to a third embodiment of the present invention;
FIG. 6 is a schematic diagram of an integration strategy of an unsupervised wafer defect detection method according to an embodiment of the present invention;
FIG. 7 is a block diagram of an unsupervised wafer defect inspection apparatus according to a first embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an unsupervised wafer defect detecting apparatus in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the unsupervised wafer defect inspection apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of an unsupervised wafer defect inspection apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and an unsupervised wafer defect inspection program.
In the unsupervised wafer defect inspection apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the unsupervised wafer defect inspection apparatus of the present invention may be disposed in the unsupervised wafer defect inspection apparatus, and the unsupervised wafer defect inspection apparatus calls the unsupervised wafer defect inspection program stored in the memory 1005 through the processor 1001 and executes the unsupervised wafer defect inspection method provided by the embodiment of the present invention.
An embodiment of the present invention provides an unsupervised wafer defect detecting method, and referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the unsupervised wafer defect detecting method according to the present invention.
In this embodiment, the unsupervised wafer defect detection method includes the following steps:
step S10: an initial wafer inspection dataset is obtained.
It should be understood that the main implementation body of this embodiment is unsupervised wafer defect detection equipment, and the unsupervised wafer defect detection equipment may be equipment such as a computer and a server, and may also be other equipment with inference capability, which is not limited in this embodiment.
It should be noted that the initial wafer inspection data set includes a small amount of labeled wafer inspection data and a large amount of unlabeled wafer inspection data, and in a specific implementation, the wafer inspection data are photoluminescence images corresponding to a plurality of defective wafers acquired by using a Photoluminescence (PL) technology, and a detection frame is framed in the corresponding photoluminescence images in advance according to a known defect type and corresponding defect type information is labeled to obtain the labeled wafer inspection data.
Step S20: and utilizing a preset unsupervised self-encoder to make pseudo labels for the unlabeled wafer detection data in the initial wafer detection data set, and generating a target wafer detection data set.
It should be understood that the preset unsupervised self-encoder infers the hidden structural features inside the data by using the unlabeled wafer detection data, and distinguishes the wafer detection data of different defect types, so as to make pseudo labels for a large amount of unlabeled wafer detection data, and generate a target wafer detection data set according to a small amount of labeled wafer detection data and a large amount of pseudo label wafer detection data. In a specific implementation, the predetermined unsupervised self-encoder distinguishes data differences between different defect types according to a spatial scale distance function, such as a mean square error function (MSE) or a root mean square error function (RMSE), where the mean square error function is expressed as:
the root mean square error function is expressed as:
preferably, the preset unsupervised self-encoder and the preset discriminator are arranged to form an anti-self-encoding network, and in the process of training and learning a large amount of unlabeled wafer detection data by the preset unsupervised self-encoder, the preset unsupervised self-encoder and the preset discriminator transmit weights through back propagation and are mutually restricted. Specifically, the preset discriminator is used for discriminating the authenticity of the pseudo label, so that whether the training of the preset unsupervised self-encoder is correct or not is determined, the pseudo label generated by the preset unsupervised self-encoder is as close to the result of the preset discriminator as possible, and the quality of the pseudo label generated by the self-encoder is improved.
Preferably, the preset discriminator is initialized in advance according to a small amount of labeled wafer detection data, so that the preset discriminator can master partial characteristics of real data, rapid fitting during model training is facilitated, and back propagation gradient dissipation is prevented.
On the other hand, in the embodiment, a plurality of depth self-encoders are arranged to be stacked to form a preset unsupervised self-encoder, thereby effectively avoiding the randomness of the network, and each depth self-encoder corresponds to an activation function and provides the nonlinear modeling capability. And aiming at the output results of the multiple depth self-encoders, combining the output results according to a preset integration strategy so as to determine the prediction result of the preset unsupervised self-encoders, and manufacturing pseudo labels for the label-free wafer detection data according to the prediction result.
Step S30: and training a preset classifier according to the target wafer detection data set to obtain the trained target classifier.
It should be understood that the target wafer inspection dataset includes a small amount of labeled wafer inspection data and a large amount of pseudo-labeled wafer inspection data, and the preset classifier is trained through the target wafer inspection dataset, so that the preset classifier learns the features for distinguishing the defect types. The preset classifier of this embodiment may be a convolutional network classifier, the convolutional network classifier is used to detect the wafer image, output a prediction result, determine a corresponding loss function value according to the prediction result and the tag information, perform iterative training until the loss function value is lower than a certain value or the current iteration number is greater than the maximum iteration number, perform defect stratification on the target wafer detection data set through the convolutional network classifier to achieve defect classification, and obtain a trained convolutional network classifier.
Step S40: and when the wafer data to be detected is received, detecting the wafer data to be detected by using the target classifier to obtain a wafer defect detection result.
It should be noted that the wafer data to be detected is a photoluminescence image acquired by a Photoluminescence (PL) technology, and is detected by using a trained target classifier, so as to determine whether the currently acquired data has defects, and determine a corresponding defect type.
In the embodiment, an initial wafer detection data set is obtained; utilizing a preset unsupervised self-encoder to make a pseudo label for the label-free wafer detection data in the initial wafer detection data set, and generating a target wafer detection data set; training a preset classifier according to the target wafer detection data set to obtain a trained target classifier; and when the wafer data to be detected is received, detecting the wafer data to be detected by using the target classifier to obtain a wafer defect detection result. By the method, the pseudo labels are made for the label-free wafer detection data, the target classifier is trained by using the labeled data and the pseudo label data, so that the target classifier learns different defect characteristics, the unknown defect types can be identified, and the detection precision of the wafer defects is improved.
Referring to fig. 3, fig. 3 is a flowchart illustrating an unsupervised wafer defect inspection method according to a second embodiment of the present invention.
Based on the first embodiment, the predetermined unsupervised self-encoder and the predetermined discriminator in the unsupervised wafer defect detecting method of the embodiment form an anti-self-encoding network;
the step S20 includes:
step S201: inputting unlabeled wafer inspection data in the initial wafer inspection dataset into the countering self-coding network.
Step S202: and predicting the label of the label-free wafer detection data by using a preset unsupervised self-encoder in the antagonistic self-encoding network, and constraining the prediction result of the preset unsupervised self-encoder by using a preset discriminator in the antagonistic self-encoding network to obtain a pseudo label corresponding to the label-free wafer detection data.
It should be understood that, in the embodiment, the discriminator is used to form a countervailing self-encoding network in combination with the self-encoder, and the countervailing self-encoding network is used to perform classification prediction on a large amount of unlabeled wafer detection data, so as to distinguish the unlabeled wafer detection data into different defect types, and at the same time, the discriminator is used to improve the quality of generating the pseudo labels from the self-encoder. The weight is transmitted by the aid of back propagation in the training process of the preset unsupervised self-encoder and the preset discriminator, and mutual restriction is achieved. The discriminator determines whether the self-encoder training is correct or not by discriminating the authenticity of the pseudo label, and the pseudo label generated by the self-encoder is as close as possible to the result of the discriminator, so that the corresponding pseudo label is made for each non-label wafer detection data.
Step S203: and generating a target wafer detection data set according to the pseudo label and the initial wafer detection data set.
In a specific implementation, a corresponding pseudo label is marked for the non-label wafer detection data in the initial wafer detection data set, and a target wafer detection data set including label wafer detection data and pseudo label wafer detection data is generated.
Further, before the step S20, the method further includes: and performing preliminary training on an initial discriminator according to the labeled wafer detection data in the initial wafer detection data set to obtain a preset discriminator.
It should be noted that, the discriminator cannot initially discriminate whether the data is true, so in this embodiment, before training the anti-self-coding network, the discriminator is initially learned, and the discriminator is initially trained by using a small amount of labeled wafer detection data, so that the discriminator can grasp partial characteristics of true data, which is beneficial to fast fitting during model training and prevents back propagation gradient dissipation.
Referring to fig. 4, fig. 4 is a schematic model layout diagram of an unsupervised wafer defect detection method according to an embodiment of the present invention, in this embodiment, a small number of labeled wafer data sets are used to initialize an unsupervised hierarchical learner, a pseudo label data set is created for a large number of wafer data sets by stacking an unsupervised self-encoder, a confrontation discriminator is trained to improve the quality of pseudo labels generated by the self-encoder, and finally, a convolutional network classifier is trained by using a large number of pseudo label data and labeled data, so as to improve detection accuracy.
In the embodiment, an initial wafer detection data set is obtained; inputting the unlabeled wafer detection data in the initial wafer detection data set into a counterself-coding network; predicting the label of the label-free wafer detection data by using a preset unsupervised self-encoder in the antagonistic self-encoding network, and constraining the prediction result of the preset unsupervised self-encoder by using a preset discriminator in the antagonistic self-encoding network to obtain a pseudo label corresponding to the label-free wafer detection data; generating a target wafer detection data set according to the pseudo label and the initial wafer detection data set; training a preset classifier according to the target wafer detection data set to obtain a trained target classifier; and when the wafer data to be detected is received, detecting the wafer data to be detected by using the target classifier to obtain a wafer defect detection result. Through the mode, the discriminator is arranged to restrain the prediction result of the unsupervised self-encoder, the generation quality of the pseudo label is improved, the labeled data and the pseudo label data are used for training the target classifier, the target classifier learns different defect characteristics, the unknown defect types can be recognized, and the training precision and the recognition precision of the target classifier are further improved.
Referring to fig. 5, fig. 5 is a flowchart illustrating an unsupervised wafer defect inspection method according to a third embodiment of the present invention.
Based on the first embodiment, the preset unsupervised self-encoder in the unsupervised wafer defect detecting method of the present embodiment is formed by stacking a plurality of self-encoders, wherein the plurality of self-encoders are set with different activation functions.
The step S20 includes:
step S204: and respectively predicting the labels of the label-free wafer detection data in the initial wafer detection data set by using a plurality of self-encoders to obtain a plurality of prediction results.
It can be understood that, in order to improve generalization performance and enable the model to learn deep features of the input data, in the embodiment, multiple depth Automatic Encoders (AEs) are stacked to form a preset unsupervised auto-encoder, which can effectively avoid randomness of the network. The self-encoders are respectively provided with different activation functions, and the activation functions are used for providing nonlinear modeling capability.
In a particular implementation, the activation function is represented as
Training sample
Determining hidden variables by activating functions for input data x
Specifically, it is expressed by the following formula:
wherein the training of the self-encoder aims at optimizing the parameter set
The reconstruction error is minimized. For the loss function of the data reconstruction error, the present embodiment is set as a traditional mean square error loss function (MSE) and a cross entropy function, where the cross entropy cost function exhibits a faster convergence speed and a stronger global optimization capability. In the case of a training sample for unlabeled m-dimensional data, the cross-entropy cost function is defined as:
preferably, considering the advantage of the activation function as a sparse processing mode in learning useful features of the model, in this embodiment, the regularized cross entropy in the automatic encoders of different activation functions rewrites the loss function of an unlabeled m-dimensional training sample into:
it should be noted that, the stacked multiple autoencoders are trained according to the above loss function, so that the multiple autoencoders perform feature layering on a large amount of unlabeled wafer inspection data.
Step S205: and determining a pseudo label corresponding to the label-free wafer detection data according to the plurality of prediction results to generate a target wafer detection data set.
It should be understood that an integration strategy is set to integrate the plurality of prediction results, a pseudo label corresponding to each non-label wafer detection data is determined, and a target wafer detection data set is generated according to the labeled wafer detection data and the pseudo label wafer detection data. Optionally, the integrated voting strategy is a majority voting strategy.
Further, after the step S205, the method further includes: determining prediction accuracies respectively corresponding to the plurality of self-encoders; deleting the self-encoders with the prediction accuracy lower than a preset threshold value from the self-encoders to obtain a plurality of residual self-encoders; and determining a pseudo label corresponding to the label-free wafer detection data according to the prediction results corresponding to the residual self-encoders to generate a target wafer detection data set.
In a specific implementation, since the majority voting policy has a defect that all the individual models have the same weight, for multiple types of defect data, the data is easily interfered by some network layer to be useless, preferably, the present embodiment sets a preset threshold for screening the stacked multiple autoencoders, where the preset threshold may be an accuracy threshold obtained by using the majority voting policy. Referring to fig. 6, fig. 6 is an integrated strategy diagram of an embodiment of the unsupervised wafer defect detection method of the present invention, in this embodiment, the accuracy judgment is performed according to a preset threshold, only a single network with an accuracy exceeding the threshold is considered in the training, other networks are discarded, and a final output result is determined according to a prediction result of an auto-encoder with an accuracy exceeding the preset threshold. Optionally, the prediction results of the remaining self-encoders are averaged to obtain a final prediction result, so as to determine a pseudo label corresponding to each non-label wafer detection data, and generate a target wafer detection data set.
Further, the determining, according to the prediction results corresponding to the multiple residual encoders, the pseudo label corresponding to the unlabeled wafer inspection data to generate a target wafer inspection data set includes: determining a weight value corresponding to each residual self-encoder according to the prediction accuracy corresponding to the residual self-encoders; and determining a pseudo label corresponding to the label-free wafer detection data according to the prediction results corresponding to the residual self-encoders and the weight values, and generating a target wafer detection data set.
It should be noted that, in this embodiment, a corresponding weight value is assigned to a network output meeting a requirement according to a corresponding prediction accuracy, and a comprehensive diagnosis result of each sample is determined according to the weight value. In a particular implementation, multiple self-encoders in a stack are trained multiple times in order to maintain a stable combined diagnostic result.
In the embodiment, an initial wafer detection data set is obtained; respectively predicting the labels of the label-free wafer detection data in the initial wafer detection data set by using a plurality of self-encoders to obtain a plurality of prediction results; determining a pseudo label corresponding to the label-free wafer detection data according to the plurality of prediction results to generate a target wafer detection data set; training a preset classifier according to the target wafer detection data set to obtain a trained target classifier; and when the wafer data to be detected is received, detecting the wafer data to be detected by using the target classifier to obtain a wafer defect detection result. Through the mode, the unsupervised self-encoder formed by stacking the self-encoders is used for generating the pseudo labels, the influence of network randomness on the generation of the pseudo labels is avoided, the generation quality of the pseudo labels is improved, the labeled data and the pseudo label data are used for training the target classifier, the target classifier learns different defect characteristics, unknown defect types can be identified, and the training precision and the identification precision of the target classifier are further improved.
In addition, an embodiment of the present invention further provides a storage medium, where an unsupervised wafer defect inspection program is stored on the storage medium, and when executed by a processor, the unsupervised wafer defect inspection program implements the unsupervised wafer defect inspection method described above.
Since the storage medium adopts all technical solutions of all the above embodiments, at least all the beneficial effects brought by the technical solutions of the above embodiments are achieved, and details are not repeated herein.
Referring to fig. 7, fig. 7 is a block diagram illustrating a structure of an unsupervised wafer defect inspection apparatus according to a first embodiment of the present invention.
As shown in fig. 7, an unsupervised wafer defect detecting apparatus according to an embodiment of the present invention includes:
an obtaining module 10 is configured to obtain an initial wafer inspection dataset.
And a label generating module 20, configured to use a preset unsupervised auto-encoder to generate a pseudo label for the unlabeled wafer inspection data in the initial wafer inspection dataset, so as to generate a target wafer inspection dataset.
And the training module 30 is configured to train a preset classifier according to the target wafer detection data set to obtain a trained target classifier.
And the detection module 40 is configured to detect the wafer data to be detected by using the target classifier when the wafer data to be detected is received, so as to obtain a wafer defect detection result.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
In the embodiment, an initial wafer detection data set is obtained; utilizing a preset unsupervised self-encoder to make a pseudo label for the label-free wafer detection data in the initial wafer detection data set, and generating a target wafer detection data set; training a preset classifier according to the target wafer detection data set to obtain a trained target classifier; and when the wafer data to be detected is received, detecting the wafer data to be detected by using the target classifier to obtain a wafer defect detection result. By the mode, the pseudo labels are made for the label-free wafer detection data, the label data and the pseudo label data are used for training the target classifier, so that the target classifier learns different defect characteristics, the unknown defect types can be identified, and the detection precision of wafer defects is improved.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the unsupervised wafer defect detection method provided in any embodiment of the present invention, and are not described herein again.
In one embodiment, the predetermined unsupervised self-encoder and the predetermined arbiter form a robust self-encoding network;
the tag generation module 20 is further configured to input the non-tag wafer inspection data in the initial wafer inspection data set to the countermeasure self-encoding network; predicting the label of the label-free wafer detection data by using a preset unsupervised self-encoder in the antagonistic self-encoding network, and constraining the prediction result of the preset unsupervised self-encoder by using a preset discriminator in the antagonistic self-encoding network to obtain a pseudo label corresponding to the label-free wafer detection data; and generating a target wafer detection data set according to the pseudo label and the initial wafer detection data set.
In an embodiment, the training module 30 is further configured to perform a preliminary training on an initial discriminator according to the labeled wafer detection data in the initial wafer detection data set, so as to obtain a preset discriminator.
In an embodiment, the pre-set unsupervised auto-encoder is formed by a stack of multiple auto-encoders, wherein the multiple auto-encoders are provided with different activation functions.
In an embodiment, the label generating module 20 is further configured to predict labels of the unlabeled wafer inspection data in the initial wafer inspection data set by using a plurality of self-encoders, respectively, to obtain a plurality of prediction results; and determining a pseudo label corresponding to the label-free wafer detection data according to the plurality of prediction results to generate a target wafer detection data set.
In an embodiment, the tag generating module 20 is further configured to determine prediction accuracies corresponding to the plurality of self-encoders, respectively; deleting the self-encoders with the prediction accuracy lower than a preset threshold value from the self-encoders to obtain a plurality of residual self-encoders; and determining a pseudo label corresponding to the label-free wafer detection data according to the prediction results corresponding to the residual self-encoders to generate a target wafer detection data set.
In an embodiment, the tag generating module 20 is further configured to determine a weight value corresponding to each remaining self-encoder according to the prediction accuracy corresponding to the plurality of remaining self-encoders; and determining a pseudo label corresponding to the label-free wafer detection data according to the prediction results corresponding to the residual self-encoders and the weight values, and generating a target wafer detection data set.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. An unsupervised wafer defect detection method, comprising:
acquiring an initial wafer detection data set;
utilizing a preset unsupervised self-encoder to make a pseudo label for the label-free wafer detection data in the initial wafer detection data set, and generating a target wafer detection data set;
training a preset classifier according to the target wafer detection data set to obtain a trained target classifier;
when wafer data to be detected are received, the target classifier is used for detecting the wafer data to be detected, and a wafer defect detection result is obtained;
the preset unsupervised self-encoder and the preset discriminator form an anti-self-encoding network;
the generating of the target wafer detection data set by using the preset unsupervised auto-encoder to make the pseudo-label for the unlabeled wafer detection data in the initial wafer detection data set comprises:
inputting unlabeled wafer inspection data in the initial wafer inspection data set to the countering self-coding network;
predicting the label of the label-free wafer detection data by using a preset unsupervised self-encoder in the antagonistic self-encoding network, and constraining the prediction result of the preset unsupervised self-encoder by using a preset discriminator in the antagonistic self-encoding network to obtain a pseudo label corresponding to the label-free wafer detection data;
and generating a target wafer detection data set according to the pseudo label and the initial wafer detection data set.
2. The unsupervised wafer defect detection method of claim 1, wherein before generating the target wafer inspection data set, the method further comprises:
and performing preliminary training on an initial discriminator according to the labeled wafer detection data in the initial wafer detection data set to obtain a preset discriminator.
3. The unsupervised wafer defect detection method of claim 1, wherein the preset unsupervised self-encoder is formed by a stack of a plurality of self-encoders, wherein the plurality of self-encoders are provided with different activation functions.
4. The unsupervised wafer defect detection method of claim 3, wherein generating the target wafer inspection dataset by creating pseudo-labels for the unlabeled wafer inspection data in the initial wafer inspection dataset using a predetermined unsupervised self-encoder comprises:
predicting labels of the label-free wafer detection data in the initial wafer detection data set by using a plurality of self-encoders to obtain a plurality of prediction results;
and determining a pseudo label corresponding to the label-free wafer detection data according to the plurality of prediction results to generate a target wafer detection data set.
5. The unsupervised wafer defect inspection method of claim 4, wherein after the labels of the unlabeled wafer inspection data in the initial wafer inspection data set are respectively predicted by a plurality of self-encoders to obtain a plurality of prediction results, the method further comprises:
determining prediction accuracies respectively corresponding to the plurality of self-encoders;
deleting the self-encoders with the prediction accuracy lower than a preset threshold value from the self-encoders to obtain a plurality of residual self-encoders;
and determining a pseudo label corresponding to the label-free wafer detection data according to the prediction results corresponding to the residual self-encoders to generate a target wafer detection data set.
6. The unsupervised wafer defect inspection method of claim 5, wherein said determining the pseudo label corresponding to the unlabeled wafer inspection data according to the prediction results corresponding to the plurality of residual autoencoders to generate a target wafer inspection data set, comprises:
determining a weight value corresponding to each residual self-encoder according to the prediction accuracy corresponding to the residual self-encoders;
and determining a pseudo label corresponding to the label-free wafer detection data according to the prediction results corresponding to the residual self-encoders and the weight values, and generating a target wafer detection data set.
7. An unsupervised wafer defect inspection device, comprising:
the acquisition module is used for acquiring an initial wafer detection data set;
the label generation module is used for making a pseudo label for the label-free wafer detection data in the initial wafer detection data set by using a preset unsupervised self-encoder to generate a target wafer detection data set;
the training module is used for training a preset classifier according to the target wafer detection data set to obtain a trained target classifier;
the detection module is used for detecting the data of the wafer to be detected by using the target classifier when the data of the wafer to be detected is received, so as to obtain a wafer defect detection result;
the preset unsupervised self-encoder and the preset discriminator form an anti-self-encoding network;
the label generation module is further configured to input the label-free wafer detection data in the initial wafer detection data set to the countermeasure self-encoding network; predicting the label of the label-free wafer detection data by using a preset unsupervised self-encoder in the antagonistic self-encoding network, and constraining the prediction result of the preset unsupervised self-encoder by using a preset discriminator in the antagonistic self-encoding network to obtain a pseudo label corresponding to the label-free wafer detection data; and generating a target wafer detection data set according to the pseudo label and the initial wafer detection data set.
8. An unsupervised wafer defect inspection apparatus, the apparatus comprising: a memory, a processor, and an unsupervised wafer defect detection program stored on the memory and executable on the processor, the unsupervised wafer defect detection program configured to implement the unsupervised wafer defect detection method of any of claims 1-6.
9. A storage medium having an unsupervised wafer defect inspection program stored thereon, the unsupervised wafer defect inspection program when executed by a processor implementing the unsupervised wafer defect inspection method of any of claims 1-6.
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