WO2023208091A1 - Procédé et appareil de configuration et d'optimisation de formule de détection, dispositif électronique et support de stockage - Google Patents

Procédé et appareil de configuration et d'optimisation de formule de détection, dispositif électronique et support de stockage Download PDF

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WO2023208091A1
WO2023208091A1 PCT/CN2023/091070 CN2023091070W WO2023208091A1 WO 2023208091 A1 WO2023208091 A1 WO 2023208091A1 CN 2023091070 W CN2023091070 W CN 2023091070W WO 2023208091 A1 WO2023208091 A1 WO 2023208091A1
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data
detection
information
defect
detection result
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PCT/CN2023/091070
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Chinese (zh)
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王敬贤
刘涛
潘成安
邓帅飞
易兵
鲁阳
张记晨
周许超
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上海微电子装备（集团）股份有限公司
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Publication of WO2023208091A1 publication Critical patent/WO2023208091A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
    • G06V10/763—Non-hierarchical techniques, e.g. based on statistics of modelling distributions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/042—Backward inferencing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30148—Semiconductor; IC; Wafer

Definitions

  • the invention relates to the field of semiconductor technology, and in particular to a detection recipe setting and optimization method, device, electronic equipment and storage medium.
  • wafer warpage (Bow) and wafer surface morphology are key parameters that affect process stability and product yield, and are critical to wafer yield (Yield). Influence. For example, after the wafer undergoes different processes such as etching or thin film deposition, the wafer will warp to varying degrees or the wafer surface will be uneven; another example is that a robot may scratch the wafer during the manufacturing process of semiconductor integrated circuits. Therefore, wafer defects are what all chip manufacturers pay most attention to during yield inspection. Once a wafer is defective, it is difficult to remedy it through subsequent processes. Therefore, it is crucial to quickly and accurately detect defects on the wafer surface to avoid wasting production resources due to defective products flowing into the next process.
  • the wafer defect detection process usually uses forward process parameter adjustment.
  • the detection parameters of the detection process are usually adjusted one by one. Adjustment, since the coupling relationship between parameters cannot be taken into account, repeated adjustments of a single parameter may lead to deviations in the parameter adjustment results.
  • the detection formula needs to repeatedly adjust parameters, which brings manpower and time. Increase in costs.
  • existing detection formulas are difficult to apply to defect detection in new processes. Adjusting the parameters of the detection formula requires a certain algorithm background, so the requirements for users are high.
  • the purpose of the present invention is to provide a detection recipe setting and optimization method, system, electronic equipment and storage medium in view of the defects existing in the prior art.
  • the detection recipe setting and optimization method provided by the invention is based on the a priori detection result data. knowledge, and fully consider the coupling relationship between parameters to determine the strategy and parameter setting values of the detection formula at one time, which not only determines the efficiency of the detection process, but also improves the detection accuracy of the detection formula.
  • the present invention provides a detection formula setting and optimization method, a detection formula setting and optimization method, including:
  • the second data sample obtain the data feature distribution information of the detection object
  • Using a preset outlier statistical analysis strategy perform outlier statistical analysis on the data feature distribution information, obtain defect distribution boundary information, and determine the detection formula according to the preset outlier statistical analysis strategy;
  • the values of the detection parameters of the detection formula are set or optimized through reverse derivation.
  • the detection result data includes basic information and characteristic data information of the detection object; wherein the characteristic data information includes position information of the detection result on the detection object, and the process flow of the detection object.
  • Information one or more of the grayscale information, shape information and texture information of the data information of the detection result;
  • Annotating the first data sample to obtain the second data sample includes:
  • For each piece of detection result data obtain the original information corresponding to the detection result data on the detection object based on the basic information of the detection object and the position information of the detection result on the detection object;
  • the detection result data is marked as true defect data; if not, the detection result data is marked as noisy data;
  • the second data sample is obtained based on all the detection result data and the label corresponding to each piece of detection result data.
  • the detection object includes a Wafer;
  • the basic information of the Wafer includes the number of the Wafer, the number of Dies it contains, and the basic information of each Die;
  • the basic information of the Die includes the Die number and the Die number of the Die. image information;
  • Obtaining the original information corresponding to the detection result data on the detection object based on the basic information of the detection object and the position information of the detection result on the detection object includes:
  • the image information of the detection result corresponding to the piece of detection result data on the Die is obtained.
  • obtaining the data feature distribution information of the detection object according to the second data sample includes:
  • the characteristic data axis represents the characteristic data information of the detection result data
  • the segmentation data axis represents the segmentation feature Information
  • the segmentation feature information includes other feature data information except for the feature data axis
  • the feature space includes one or more feature data axes and one or more segmentation data axes.
  • arranging the second data samples according to the feature space to obtain data feature distribution information of the detection object includes:
  • the characteristic value size of the characteristic data information represented by the characteristic data axis in the horizontal axis direction, the characteristic value size of the characteristic data information represented by the characteristic data axis, and in the vertical axis direction, according to the characteristic data represented by the segmented data axis.
  • the second data samples are arranged according to the characteristic value size of the information to obtain a defect characteristic distribution map.
  • using a preset outlier statistical analysis strategy to perform outlier statistical analysis on the data feature distribution information to obtain defect distribution boundary information includes:
  • training the outlier statistical analysis model includes: training the selected outlier statistical analysis model according to the detection result data and the data feature distribution information until the obtained The defect distribution boundary information of the detection object satisfies the first preset condition;
  • the use of data segmentation method to perform outlier statistical analysis on the data feature distribution information includes: based on the detection result data and the data feature distribution information, on the feature data axis and/or the segmented data axis Obtain at least one first segmentation threshold; and obtain the defect boundary information according to the first segmentation threshold until the obtained defect distribution boundary information of the detection object satisfies the second preset condition.
  • the segmented data axis represents process flow information; and based on the detection result data and the data feature distribution information, threshold segmentation is performed on the characteristic data axis and/or the segmented data axis until the The defect distribution boundary information of the detection object satisfies the second preset condition, including:
  • the defect distribution boundary information of the detection object is obtained.
  • preset outlier statistical analysis strategies also includes: an outlier statistical analysis strategy that combines data segmentation and model learning;
  • the outlier statistical analysis strategy that combines data segmentation and model learning includes: obtaining at least one first segmentation threshold on the segmentation data axis of the detection result data labeled as a true defect based on the data feature distribution information. ; And according to the first segmentation threshold and the data feature distribution information, train the selected outlier statistical analysis model until the obtained defect distribution boundary information of the detection object meets the third preset condition.
  • setting or optimizing the values of detection parameters of the detection formula through reverse derivation based on the defect distribution boundary information and the preset outlier statistical analysis strategy including:
  • the values of the detection parameters of the detection recipe are set or optimized.
  • the preset outlier statistical analysis strategy is a data segmentation method
  • the data distribution density of the detection result data of the detection object is counted as the reverse derivation strategy
  • all detection result data of the detection object are used as the input data information
  • the data distribution density of the characteristic data information of all the detection result data in the feature space is divided into normal areas, noise areas and true defect areas;
  • the normal area is where the data distribution density is greater than The area of the first density threshold
  • the noise area is the area where the data density is less than or equal to the first density threshold and greater than the second density threshold
  • the true defect area is the area where the data density is less than or equal to the second density threshold
  • the preset outlier statistical analysis strategy is an outlier statistical analysis strategy based on Gaussian model
  • the Gaussian distribution of the detection result data of the detection object is obtained as the reverse derivation strategy, and Gaussian model detection is used as the detection formula strategy;
  • all detection result data of the detection object are used as the input data information and the defect distribution boundary information is used as the input data information;
  • the parameters of the Gaussian model detection are determined.
  • the preset outlier statistical analysis strategy is a machine learning outlier statistical analysis strategy
  • the density threshold and distance threshold for obtaining the detection result data of the detection object are used as the reverse derivation strategy, and the machine learning model is used as the strategy of detection formula;
  • the obtained density and distance of the detection result data of the detection object are used as the input data information
  • the density parameters and distance parameters of the detection strategy of the machine learning model are reversely derived.
  • the detection recipe setting and optimization method also includes:
  • defect analysis of the object to be detected is performed to obtain defect data information of the object to be detected.
  • the detection parameter and adjustment device includes:
  • the true defect and noise marking unit is configured to mark the first data sample to obtain a second data sample; wherein the first data sample includes several pieces of detection result data; the second data sample includes the detection result data Result data and labels corresponding to each test result data;
  • a feature distribution information acquisition unit configured to obtain data feature distribution information of the detection object based on the second data sample
  • the defect distribution boundary acquisition unit is configured to use a preset outlier statistical analysis strategy to perform outlier statistical analysis on the data feature distribution information, obtain defect distribution boundary information, and is used to perform outlier statistical analysis according to the preset outlier statistical analysis strategy, Determine the test formula;
  • the detection parameter setting and optimization unit is configured to determine or optimize the value of the detection parameter of the detection formula through reverse derivation based on the defect distribution boundary information and the preset outlier statistical analysis strategy.
  • the detection recipe setting and optimization device also includes:
  • the detection recipe application unit is configured to perform defect analysis on the object to be detected based on the detection formula and the values of detection parameters of the detection formula, and obtain defect data information of the object to be detected.
  • the present invention also provides an electronic device, including a processor and a memory.
  • a computer program is stored on the memory.
  • the computer program is executed by the processor, the above-mentioned detection recipe setting is realized. and optimization methods.
  • the present invention also provides a readable storage medium.
  • a computer program is stored in the readable storage medium.
  • the computer program is executed by the processor, the detection recipe setting and optimization method described above is realized. .
  • the detection recipe setting and optimization method, device, electronic equipment and storage medium provided by the present invention have the following advantages:
  • the detection recipe setting and optimization method provided by the present invention first obtains a second data sample by annotating the first data sample; wherein the first data sample includes several pieces of detection result data; the second data sample includes all The detection result data and the label corresponding to each of the detection result data; then obtain the data feature distribution information of the detection object according to the second data sample, and determine the detection formula according to the preset outlier statistical analysis strategy; Then, a preset outlier statistical analysis strategy is used to perform outlier statistical analysis on the data feature distribution information to obtain defect distribution boundary information; finally, according to the defect distribution boundary information and the preset outlier statistical analysis strategy, through inverse Through direct derivation, the detection formula determines or optimizes the detection parameters of the detection formula.
  • the first data sample includes several pieces of detection result data
  • the detection result data includes auxiliary parameter adjustment information (such as the basic information and characteristic data of the detection object).
  • Information, the characteristic data information includes but is not limited to the grayscale, shape, texture and other information of the defects indicated by the detection results).
  • true defect data and noise data can be distinguished, which can effectively utilize historical information for subsequent data analysis and analysis. Inference provides an important basis for obtaining accurate prior knowledge, which can improve the detection accuracy of detection formulas.
  • the detection recipe strategy and detection parameter values are obtained through reverse derivation based on the defect distribution boundary information and the preset outlier statistical analysis strategy.
  • the present invention can deduce a set of detection parameters at the same time through reverse derivation (that is, adjust all parameters at the same time).
  • the coupling relationship between parameters is also taken into account, and the detection formula is realized. Rapid modeling; avoids repeated adjustment of parameters, which can significantly save manpower and time costs; moreover, for new process defect detection, users can set or optimize the strategy of the detection formula and the detection parameters of the detection formula without having any algorithm foundation. Take value.
  • the detection recipe setting and optimization device, electronic equipment and storage medium provided by the present invention and the detection parameters and adjustment method provided by the present invention belong to the same inventive concept, therefore, the detection recipe setting and optimization device, electronic equipment and storage medium provided by the present invention It has all the advantages of the detection recipe setting and optimization method, which will not be described in detail here.
  • Figure 1 is a schematic flow chart of a detection recipe setting and optimization method provided by an embodiment of the present invention
  • Figure 2 is a schematic flow chart of a data sample labeling method provided by an embodiment of the present invention.
  • Figure 3 is a schematic diagram of an interface for defect marking of data samples provided by an embodiment of the present invention.
  • Figure 4 is an example diagram showing the distribution of detection result data in a two-dimensional feature space in one specific example of applying the present invention
  • Figure 5 is a schematic diagram of the principle of outlier statistical analysis provided by an embodiment of the present invention.
  • Figure 6 is a schematic diagram of defect distribution boundary information obtained by applying the outlier statistical analysis model provided by the present invention.
  • FIG. 7 is a detailed flow diagram of step S400 in Figure 1;
  • Figure 8 is a specific example diagram of reverse derivation using the detection formula setting and optimization method provided by the present invention.
  • Figure 9 is a schematic diagram of the data density distribution of one of the detection result data provided by an embodiment of the present invention.
  • Figure 10 is a schematic diagram of true defect data distribution within the average gray level range of the standard segmentation axis provided by an embodiment of the present invention.
  • Figure 11(a) is an example of multiple test charts provided by an embodiment of the present invention.
  • Figure 11(b) is an example of the mean graph generated from multiple test images in Figure 11(a);
  • Figure 11(c) is an example of the standard deviation chart generated from multiple test charts in Figure 11(a);
  • Figure 11(d) is an enlarged example of one of the test images
  • Figure 11(e) is a schematic diagram of defect locations detected using machine learning recipes
  • Figure 12 is a schematic diagram of the grayscale dynamic threshold provided by the application of the present invention.
  • Figure 13 is a schematic diagram comparing the detection result data obtained by applying the detection formula setting and optimization method provided by the present invention and the detection result data obtained by the original detection formula;
  • Figure 14 is a structural block diagram of a detection recipe setting and optimization device in an embodiment of the present invention.
  • FIG. 15 is a schematic block structure diagram of an electronic device in an embodiment of the present invention.
  • 100-True defect and noise marking unit 200-Feature distribution information acquisition unit, 300-Defect distribution boundary acquisition unit, 400-Inspection parameter setting and optimization unit, 500-Inspection recipe application unit;
  • 601-processor 602-communication interface, 603-memory, 604-communication bus.
  • FIG. 1 schematically provides a flow chart of the detection recipe setting and optimization method provided by an embodiment of the present invention.
  • the detection recipe setting and optimization method includes the following steps:
  • S100 Annotate the first data sample to obtain a second data sample; wherein the first data sample includes several pieces of detection result data; the second data sample includes the detection result data and each of the detection results The label corresponding to the data;
  • S300 Use a preset outlier statistical analysis strategy to perform outlier statistical analysis on the data feature distribution information, obtain defect distribution boundary information, and determine the detection formula according to the preset outlier statistical analysis strategy;
  • S400 Based on the defect distribution boundary information and the preset outlier statistical analysis strategy, set or optimize the values of the detection parameters of the detection formula through reverse derivation.
  • the first data sample includes several pieces of detection result data, and the detection result data includes a large amount of auxiliary parameter adjustment information (such as the basic information and characteristics of the detection object).
  • Data information, the characteristic data information includes but is not limited to the grayscale, shape, texture and other information of the defects indicated by the detection results).
  • real defect data and noise data can be distinguished, and the historical information can be effectively used for subsequent data analysis.
  • reasoning can provide an important basis for obtaining accurate prior knowledge, which can improve the detection accuracy of detection formulas.
  • the detection recipe strategy and parameter setting values are obtained through reverse derivation based on the defect distribution boundary information and the preset outlier statistical analysis strategy.
  • the present invention can deduce a set of detection parameters at the same time (that is, adjust all parameters at the same time) through reverse derivation.
  • the coupling relationship between parameters is also taken into account, realizing rapid modeling of the detection process and avoiding repeated adjustments. parameters, which can significantly save labor and time costs.
  • users can set or optimize the detection recipe strategy and detection parameter values without having any algorithm foundation.
  • the detection result data is the historical detection result data of the detection object.
  • the detection data is detection result data (that is, the first sample data).
  • the detection result data includes all or part of the historical detection data of the detection formula to be optimized.
  • the detection result data described below are historical detection data of wafer defects. Obviously, this is not a limitation of the present invention.
  • the detection recipe setting and optimization provided by the present invention The method can also be adapted to other detection formulas for initial detection of wafer defects, so no examples will be given one by one.
  • the detection result data includes basic information and characteristic data information of the detection object; wherein the characteristic data information includes position information of the detection result on the detection object, and one or more of the process flow information of the detection object, the grayscale information, the shape information and the texture information of the data information of the detection result.
  • the data information of the detection results must also include conclusion information (defective data or non-defective data) used to indicate the detection results.
  • conclusion information defective data or non-defective data
  • the detection result data includes the basic information and characteristic data information of the detection object (such as the grayscale, shape, texture and other information of nuisance) and other auxiliary parameter adjustment information, and will be used in the subsequent drawing of the defect distribution map.
  • the parameter reverse reasoning process is based on the detection result data. Therefore, the detection formula setting and optimization method provided by the present invention can improve the detection accuracy of the detection formula.
  • Figure 2 schematically shows a schematic flow chart of the data sample annotation method.
  • the first data sample is annotated to obtain the second data sample, including:
  • S120 For each piece of detection result data, obtain the original information corresponding to the detection result data on the detection object based on the basic information of the detection object and the position information of the detection result on the detection object;
  • S130 Based on the original information, determine whether the defect marked by the data information of the detection result is a true defect. If so, mark the detection result data as true defect data; if not, mark the detection result data as true defect data. Marked as noisy data;
  • S140 Obtain the second data sample based on all the detection result data and the tag corresponding to each detection result data.
  • the detection recipe setting and optimization method provided by the present invention can accurately distinguish the real defect data and noise data (nusiance, noise interference) in the detection result data (historical data) by labeling the first data sample.
  • Data are accurately distinguished, thereby providing accurate prior knowledge for subsequent acquisition of data feature distribution information, and further obtaining defect distribution boundary information based on the data feature distribution information for further reverse derivation, thereby improving the detection accuracy of the detection formula.
  • the characteristic data information is all detection results of defect detection on the detection object, including defect data and non-defect data.
  • the detection object as a wafer as an example.
  • the first data sample is the wafer. historical test result data.
  • the basic information of the Wafer includes the number of the Wafer, the number of Dies (die) contained, and the basic information of each Die; the basic information of the Die includes the Die number and image information of the Die.
  • the original information corresponding to the detection result data on the detection object is obtained based on the basic information of the detection object and the position information of the defect on the detection object, including:
  • S121 According to the basic information of the Wafer, obtain the Die number of each Die of the Wafer and the basic information of each Die;
  • the data information of the detection result includes the detection result in the detection result data.
  • description of the image information, and the image information of the detection result is the original image corresponding to the data information of the detection result on the detection object.
  • the data information of the detection result includes the image information of the detection result. data expression.
  • the data information of the detection result records the texture characteristics of the texture defect, such as the roughness of the texture, etc., and the data information of the detection result
  • the image information is the original image corresponding to the texture defect. Therefore, according to the image information of the detection result, the detection result data corresponding to the image information of the detection result can be re-judged whether it is true defect data or noise data. .
  • FIG. 3 schematically illustrates one of the interface diagrams for defect marking of data samples provided by an embodiment of the present invention.
  • the Wafer display window area is used to graphically display the basic information of the Wafer, including but not limited to the position of each Die on the Wafer and the number of the Die.
  • the user can select the Die number to be marked for defects. According to the Die number selected by the user, the historical detection data results of the Die corresponding to the selected Die number will be refreshed in the detection data list window area.
  • the user can select the detection result data one by one, and the original information corresponding to the detection result data (i.e., the defect display area) will be displayed.
  • the image information of the detection result is the image information indicated by the position information of the detection result on the Die). Therefore, according to various characteristics of the original information (texture, size, curvature, shape, etc.), it can be artificially Further confirm whether the defect indicated by the data information of the test result is a true defect by means of re-judgment or machine re-judgment.
  • the piece of test result data is marked as true defect data (for example, it will be included in the test data list
  • the label of the detection result data in the window area is marked as a true defect, and the value corresponding to the column of the manual judgment whether it is a real defect is set to yes); if not, the detection result data is marked as noise data (for example, the value in the The label of the detection result data in the detection data list window area is marked as a false defect, and the value corresponding to the manual judgment whether it is a real defect column is set to No).
  • the detection recipe setting and optimization method provided by the present invention is explained by taking a wafer as an example as a detection object, as those skilled in the art can understand, this is only a preferred embodiment.
  • the detection object may also be other products besides wafers, including but not limited to lenses, display screens, 3D printing products, etc. Explain with examples one by one.
  • step S200 obtaining the data feature distribution information of the detection object based on the second data sample includes:
  • S210 Determine the characteristic data axis and the segmented data axis, and establish a feature space based on the characteristic data axis and the segmented data axis; wherein the characteristic data axis represents the characteristic data information of the detection result data, and the segmented data axis represents Segmentation feature information; wherein the segmentation feature information includes other feature data information except for the feature data axis;
  • S220 Arrange the second data samples according to the feature space to obtain data feature distribution information of the detection object.
  • the detection recipe setting and optimization method provided by the present invention arranges the second data samples through the feature space, and the purpose is to make the distribution of the detection result data in the feature space show a certain trend. , making the distinction between true defect data and noise data more obvious, so as to facilitate the acquisition of defect distribution boundary information.
  • the feature space includes one or more feature data axes and one or more segmentation data axes.
  • the feature space may include multiple feature data axes and multiple segmentation data axes, and the feature space may be a multi-dimensional feature space.
  • step S220 the Arrange the second data samples to obtain the data feature distribution information of the detection object, including:
  • S221 Use the feature data axis as the horizontal axis and the segmented data axis as the vertical axis to establish a rectangular coordinate system;
  • S222 In the rectangular coordinate system, in the horizontal axis direction according to the characteristic value size of the characteristic data information represented by the characteristic data axis, and in the vertical axis direction according to the characteristic value represented by the segmented data axis.
  • the second data samples are arranged according to the characteristic value size of the characteristic data information to obtain a defect characteristic distribution map.
  • Figure 4 schematically shows an example diagram of the distribution of detection result data in a two-dimensional feature space of one specific example.
  • the horizontal axis represents the feature data axis
  • the vertical axis represents the two-dimensional data feature distribution map formed by dividing the data axis. That is, the abscissa of each point in the coordinate system represents the size of the feature value, and the ordinate represents the size of the corresponding segmentation feature value.
  • the feature values of all detection result data constitute the entire feature distribution map.
  • the feature data axis and the segmentation data axis may be multi-dimensional. That is, multiple segmentation values can be selected for the segmented data axis to divide the detection result data (ie, the second sample data) into several different feature distributions.
  • the present invention does not limit the specific selection method of the feature space.
  • a feature selection algorithm can be used to select the feature data axis and the segmentation data axis to automatically select the feature space; in other embodiments, , the feature data axis and segmentation data axis can also be selected manually, and the present invention does not impose any limitations on this.
  • the feature data axis can represent information such as color, texture, shape, size, etc.
  • the segmentation axis can be information such as a trained mean map.
  • the criteria for selecting the feature space are: the segmented data axis can better distinguish different process areas, and the feature data axis can make true defect data and noise There are obvious differences between the data (noise points).
  • the ultimate goal is to make the distribution of the detection result data in the feature space show a certain trend, making the distinction between real defects and noise points more obvious.
  • the detection result data of wafer defects if the shape in the feature data information is used as the feature data axis rather than the texture in the feature data information as the feature data axis, the detection result data can be better positioned in the feature space.
  • the shape in the feature data information is used as the feature data axis instead of the texture in the feature data information as the feature data axis. It can be understood that the shape in the feature data information is no longer used as the segmentation data axis.
  • FIG. 5 schematically provides a flow chart of a detection recipe setting and optimization method provided by an embodiment of the present invention. It can be seen from Figure 5 that in step S300, the preset outlier statistical analysis strategy is used to perform outlier statistical analysis on the data feature distribution information to obtain defect distribution boundary information, including:
  • FIG. 6 is a schematic diagram of defect distribution boundary information obtained by applying the outlier statistical analysis model provided by the present invention.
  • feature1 is the segmentation data axis
  • feartrue2 is the feature data axis.
  • the defect distribution boundary information 3 is a curve. It can be seen that the detection formula setting and optimization method provided by the present invention determines the preset outlier statistical analysis strategy based on the detection result data and the data feature distribution information, and determines the preset outlier statistical analysis strategy based on the determined preset outlier.
  • the group statistical analysis strategy performs outlier statistical analysis on the data feature distribution information to obtain defect distribution boundary information, which can enable the defect distribution boundary information to better separate the true defect data 2 and the noise data 1, that is,
  • the defect distribution boundary information can reduce over-inspection problems as much as possible without causing missed detection defects, so as to filter out more noise data. This can ensure that the subsequent detection formula determined by reverse derivation based on the defect distribution boundary information will not cause missed detection or over-detection, thereby improving the defect detection accuracy of the detection process.
  • the defect distribution boundary information obtained may be different. Therefore, the subsequent reverse Derivation As well as the strategy for detecting formulas are closely related to the outlier statistical analysis strategy.
  • the shape of the defect distribution boundary information is completely different from that in Figure 6 , please refer to the description below for details. To avoid redundancy, we will not elaborate here.
  • training the outlier statistical analysis model includes: training the selected outlier statistical analysis model according to the detection result data and the data feature distribution information until the obtained The defect distribution boundary information of the detection object satisfies the first preset condition.
  • the outlier analysis statistical model includes but is not limited to Statistics-based outlier algorithms (such as the 3 ⁇ principle), distance and proximity-based clustering algorithms (such as K-means, etc.), density-based outlier algorithms (such as DBSCAN, etc.), tree-based outlier analysis algorithms (such as isolated forest, etc.). It should be noted that the choice of algorithm model is very critical. Different algorithm models mean different shapes of outlier boundaries. An optimal algorithm model can make the training of the data set neither underfitting nor outliers occur. Overfitting.
  • the outlier analysis statistical model is preferably based on a statistical outlier algorithm (such as the 3 ⁇ principle).
  • a statistical outlier algorithm such as the 3 ⁇ principle.
  • the outlier analysis statistical model is preferably based on the distance sum Proximity clustering algorithm.
  • the purpose of the outlier analysis statistical model is to find the optimal boundary result.
  • the second sample data should be used to pair the selected
  • the outlier analysis statistical model is trained, and through continuous learning and target optimization processes, the model training results can find the optimal inflection point of the segmented data axis and classify the true defects and noise data ( interference noise points) to distinguish. Therefore, after the training of the outlier analysis statistical model is completed, a boundary result (ie, defect distribution boundary information) is obtained.
  • a boundary result ie, defect distribution boundary information
  • defect distribution boundary curve 3 ie, defect distribution boundary information
  • the first preset condition is that the defect distribution boundary information can distinguish the detection result data labeled as true defect data and the detection result data labeled as noise data in the second sample.
  • using the data segmentation method to perform outlier statistical analysis on the data feature distribution information includes: based on the detection result data and the data feature distribution information, on the feature data axis and/or the segmented data At least one first segmentation threshold is obtained on the axis; and the defect boundary information is obtained according to the first segmentation threshold until the obtained defect distribution boundary information of the detection object satisfies the second preset condition.
  • the data segmentation method includes manually segmenting the feature space to obtain the first segmentation threshold.
  • the present invention is not limited to the specific implementation of the data segmentation method.
  • the first segmentation threshold can also be obtained through a data segmentation algorithm.
  • S321 Determine the first segmentation threshold of the segmented data axis based on the data feature distribution information and the consistency of the data distribution of the detection results labeled as true defect data and labeled as noise data.
  • S322 Determine the second segmentation threshold of the feature data axis based on the data feature distribution information and the consistency of the data distribution of the detection results labeled as true defect data and labeled as noise data;
  • S323 Obtain the defect distribution boundary information of the detection object based on the first segmentation threshold of the segmentation data axis and the second segmentation threshold of the feature data axis.
  • step S321 the data feature distribution information is used as input, and the segmented data axis is segmented in this feature distribution map.
  • the segmentation standard is the consistency of the detection result data distribution, and the data with consistent distribution is regarded as a Cluster, find the segmentation value between clusters, so that the data of different processes can be distinguished.
  • the consistent distribution includes the distribution law of the characteristic data information of the detection result data, including but not limited to the distribution density in the characteristic space, the relative position relationship of the spatial points, etc., based on which the segmentation axis and the characteristic axis threshold are determined, such as , in one of the examples, two first segmentation thresholds segment_value1 and segment_value2 are set.
  • a second segmentation threshold is determined for the feature data axis in the feature distribution. Since the defect data points have been marked in the feature distribution, the principle of determining the second segmentation threshold is to separate the noise data and the real defect data as far as possible, so as to ensure that the detection result data will not be missed at the same time. Also minimize the occurrence of over-inspections. That is, the second preset condition is preferably that the defect boundary information can separate the true defect data and the noise data.
  • the defect respective boundary information of the outlier statistical analysis can be obtained.
  • the figure below still takes the two-dimensional feature data distribution as an example to display the manually segmented defect distribution boundary information.
  • the detection result data is segmented using two first segmentation thresholds segment_value1 and segment_value2 on the segmentation axis, and all the detection result data is divided into three different distributions. In each segmentation threshold interval, three different second segmentation thresholds are used on the feature data axis to distinguish true defects from noise data, and the final defect distribution boundary information is obtained.
  • the defect distribution boundary information includes two straight lines parallel to the feature data axis featureu1 formed by the two first segmentation thresholds segment_value1 and segment_value2, and are respectively located on the feature data axis featreu1 and the first segmentation thresholds segment_value1 and segment_value2.
  • the defect distribution boundary information includes two straight lines parallel to the feature data axis featureu1 formed by the two first segmentation thresholds segment_value1 and segment_value2, and are respectively located on the feature data axis featreu1 and the first segmentation thresholds segment_value1 and segment_value2.
  • the use of a preset outlier statistical analysis strategy further includes: an outlier statistical analysis strategy that combines data segmentation and model learning.
  • the outlier statistical analysis strategy that combines data segmentation and model learning includes: obtaining at least one first segmentation threshold on the segmentation data axis of the detection result data labeled as a true defect based on the data feature distribution information. ; And according to the first segmentation threshold and the data feature distribution information, train the selected outlier statistical analysis model until the obtained defect distribution boundary information of the detection object meets the third preset condition.
  • the detection recipe setting and optimization method provided by the present invention can further reduce the uncertainty of machine learning model training through an outlier statistical analysis strategy that combines data segmentation and model learning when obtaining outlier distribution boundary information.
  • the input of the machine learning model has certain constraints, and the results of manual segmentation are used as constraints, which can further improve the efficiency of obtaining defect boundary distribution information.
  • the third preset condition is preferably to ensure that the detection result data does not miss detection while also minimizing the occurrence of over-inspection. That is, the second preset condition is preferably that the defect boundary information can reduce the true defect to The data and the noise data are separated or the number of training times of the outlier statistical analysis model reaches a preset value.
  • the defect distribution boundary information obtained by using the outlier statistical analysis strategy that combines data segmentation and model learning is different from the defect distribution boundary information obtained by the above-mentioned data segmentation method.
  • the defect distribution boundary information obtained by the outlier statistical analysis strategy that combines data segmentation and model learning includes two straight lines parallel to the feature data axis featureure1 formed by the two first segmentation thresholds segment_value1 and segment_value2, and two straight lines located on the feature data respectively.
  • the three intervals formed by axis featreu1, the first segmentation thresholds segment_value1 and segment_value2 are closed curves surrounding the true defect data. Due to the different outlier statistical analysis strategies used, the defect boundary distribution information obtained is completely different.
  • the defect boundary distribution information obtained can all compare with the detection result data. middle Accurately distinguish between true defect data and noise data. As mentioned above, based on this, the present invention does not limit the specific implementation of the outlier statistical analysis strategy.
  • step S400 based on the defect distribution boundary information and the preset outlier statistical analysis strategy, through reverse derivation, the values of the detection parameters for setting or optimizing the detection formula are determined, including :
  • S410 Determine the reverse derivation strategy according to the preset outlier statistical analysis strategy
  • S430 Determine the data distribution model of the detection result data according to the input data information
  • S440 Determine the detection parameters of the detection formula according to the data distribution model and the defect distribution boundary information
  • S450 Set or optimize the value of the detection parameter of the detection recipe according to the strategy of the detection recipe and the input data information of the reverse derivation.
  • the detection recipe setting provided by the present invention is different from Optimization method uses reverse derivation to determine the detection recipe strategy, and reversely infers all parameter settings of the detection recipe (key parameters, such as data density, data sparsity distance and/or tolerance range, etc.) based on the defect boundary distribution information ), the coupling relationship between the parameters of the detection process is also taken into account, thereby avoiding repeated parameter adjustment processes; and the parameter adjustment process is based on the user's annotation results, and the user does not need to have prior knowledge to automatically deduce a relatively accurate set of parameters.
  • the parameters of the detection process are adjusted to the optimal level at one time, which not only improves the efficiency of parameter adjustment in the detection process, but also improves the detection accuracy of the detection formula.
  • FIG. 8 schematically shows a specific example of reverse derivation using the detection recipe setting and optimization method provided by the present invention.
  • the outlier statistical analysis strategy, the reverse derivation strategy and the parameter setting values of the detection process are closely related: that is, the reverse derivation strategy
  • the strategy for directional derivation and the strategy for detecting recipes are consistent with the core of the outlier statistical analysis strategy for obtaining the defect boundary distribution information.
  • the outlier segmentation method is used as the strategy for outlier statistical analysis
  • the basic principles of the strategy for reverse derivation and detection of recipes should also be consistent with the basic principles of the outlier segmentation method.
  • the following uses the data segmentation method as the outlier statistical analysis strategy, the outlier statistical analysis strategy based on Gaussian model and the outlier statistical analysis strategy of machine learning as examples to perform reverse derivation to obtain the parameters of the detection formula.
  • the process of setting values is explained in detail.
  • FIG. 9 schematically provides a schematic diagram of the data density distribution of one of the detection result data provided in an implementation manner of this embodiment.
  • the basic idea of this method is to define the area where the density of the detection result data points (the characteristic value of the detection result data) in the feature distribution diagram is greater than the first threshold as a normal area, that is, the normal area is expressed as the sum of the data density related functions. Therefore, all data points (feature values of detection result data) whose data density data_density is greater than the first threshold are normal, and then data density data_density is one of the detection parameters that requires reverse inference.
  • an area where the data density is less than or equal to the first threshold and greater than the second threshold is defined as a nuisance area.
  • boundary threshold is the defect distribution boundary result obtained by the outlier statistical analysis algorithm
  • defect_threshold is the function related to the defect distribution boundary boundary_threshold
  • displacement parameter offset_parameter can be calculated using defect_threshold and nuisance_threshold.
  • the preset outlier statistical analysis strategy is the data segmentation method
  • the displacement parameters of the detection formula are obtained through the following steps:
  • Step A1 According to the data segmentation method, count the data distribution density of the detection result data of the detection object as the reverse derivation strategy.
  • Step A2 According to the reverse derivation strategy of the statistical data distribution density, use all detection result data of the detection object as the input data information.
  • Step A3 Based on all the detection result data, it is assumed that the data distribution density of the characteristic data information of all the detection result data in the feature space is divided into normal areas, noise areas and true defect areas; the normal area is the data The area where the distribution density is greater than the first density threshold.
  • the noise area is the area where the data density is less than or equal to the first density threshold and greater than the second density threshold.
  • the true defect area is the area where the data density is less than or equal to the second density threshold. area.
  • Step A4 Calculate the first density threshold and the second density threshold according to all detection result data and the labels of all detection result data; wherein the first density threshold is greater than the second density threshold;
  • Step A5 Calculate the displacement parameter of the detection formula according to the first density threshold, the second density threshold and the defect distribution boundary information.
  • the data segmentation method is used to obtain defect boundary distribution information, and reverse derivation is performed to obtain the parameter setting values of the inspection process.
  • FIG. 10 a schematic diagram of true defect distribution within the average gray level range of the standard segmentation axis provided by an implementation of this embodiment is provided.
  • Figure 11(a)- Figure 11(c) and Figure 12 are examples of multiple test charts provided by an embodiment of the present invention, and Figure 11(b) is Figure 11 (a) is an example of the average value chart generated by multiple test charts. Figure 11(c) is an example of the standard deviation chart generated by multiple test charts in Figure 11(a).
  • Figure 12 is the grayscale provided by the application of the present invention. Dynamic threshold diagram. In the figure, pixel A is a pixel in the test image, and pixel A1 and A2 are the corresponding pixels of pixel A in the mean map and standard deviation map respectively.
  • Selected samples Select samples (as shown in Figure 11(a)), and obtain the average value chart and the standard deviation chart based on statistics (training) of N test charts.
  • feature1 is the feature data axis in Figure 10
  • test is the gray value of the test image
  • mean is the gray value of the average image obtained by statistics of N test images.
  • mean is the gray value of the average image obtained by statistics of N test images.
  • defect_threshold mean+/-(sigma*std+gray) (7)
  • mean is the gray value of the average image obtained by statistics of N test images
  • std is the standard deviation corresponding to one of the pixels in the test image
  • sigma is the coefficient of the standard deviation
  • gray is the dynamic threshold.
  • the dynamic threshold gray is equivalent to the displacement parameter offset_parameter mentioned above, which can be defined as any curve.
  • pixels greater than the above threshold defect_threshold are normal points, and pixels less than or equal to the threshold defect_threshold are defective points.
  • Figure 11(d) is an enlarged example of one of the test images
  • Figure 11(e) is the defect location detected using a machine learning algorithm. Schematic diagram. By comparing Figure 11(d) and Figure 11(e), it is easy to find that the detection formula obtained by using the detection formula setting and optimization method provided by the present invention can accurately detect the true defects of the object to be detected.
  • the outlier statistical analysis strategy based on the Gaussian model is first used to reversely deduce new data.
  • the core ideas of the process and parameter setting values are explained.
  • the basic principle of this method is to assume that the distribution of all data points (detection result data) in the feature distribution map obeys Gaussian distribution.
  • the parameters such as mean, variance and variance coefficient that need to be used in the detection model (strategy of the detection process) are reversely inferred to obtain the correlation required for Gaussian model detection. parameter.
  • the Gaussian model-based outlier statistical analysis strategy to reversely derive new data processes and parameter settings includes the following steps:
  • Step B1 The preset outlier statistical analysis strategy is an outlier statistical analysis strategy based on Gaussian model
  • Step B2 According to the outlier statistical analysis strategy based on the Gaussian model, use the Gaussian distribution of the detection result data of the detection object as the reverse derivation strategy, and use Gaussian model detection as the detection formula strategy;
  • Step B3 According to the reverse derivation strategy of statistical Gaussian distribution, use all detection result data of the detection object as the input data information and the defect distribution boundary information as the input data information;
  • Step B4 Based on all the detection result data, it is assumed that the data distribution density of the feature values of all the feature data information of the detection result data in the feature space obeys Gaussian distribution;
  • Step B5 Determine the parameters of the Gaussian model detection based on the input data information and the defect distribution boundary information.
  • boundary_threshold is the defect boundary distribution result obtained by the outlier algorithm.
  • This boundary matrix boundary_threshold can already be obtained.
  • the mean ⁇ can be obtained from the detection result data, which is obtained by calculating the average gray level of the current detection data image.
  • the variance ⁇ is calculated by subtracting the sum of squares from the gray value of the pixels of the image to be detected and the mean ⁇ , and then averaging.
  • the outlier statistical analysis strategy based on machine learning to reversely derive new data processes and parameter setting values includes the following steps:
  • Step C1 The preset outlier statistical analysis strategy is a machine learning outlier statistical analysis strategy
  • Step C2 According to the outlier statistical analysis strategy of machine learning, the density threshold and distance threshold for obtaining the detection result data of the detection object are used as the reverse derivation strategy, and the machine learning model is used as the detection formula strategy;
  • Step C3 According to the reverse derivation strategy of obtaining the density threshold and distance threshold of the detection result data of the detection object, use the obtained density and distance of the detection result data of the detection object as the input data information;
  • Step C4 Based on all detection result data and the defect boundary distribution information, reversely derive the density parameters and distance parameters of the detection strategy of the machine learning model.
  • the determination of the parameters of the outlier statistical analysis algorithm based on machine learning directly affects the detection accuracy.
  • the initial clustering center in the k-means algorithm, the neighborhood and number threshold in the DBSCAN algorithm, etc. Therefore, by performing reverse reasoning on these machine learning parameters through the defect boundary distribution information (results) in outlier statistical analysis, a machine learning model with prior knowledge can be obtained, thereby improving the accuracy of model detection.
  • boundary_threshold is the defect boundary distribution information obtained by the outlier algorithm, which is related to the detection result data and has been obtained in the defect boundary analysis process.
  • the two important parameters of the clustering algorithm based on distance and density are density density_parameters and distance distance_parameters. Density density_parameters and distance distance_parameters are derived from the detection result data and the boundary matrix. By inverting the distance and density parameters, the defects are exactly located at the preset threshold. can be detected; while normal pixels are located within a threshold range with a larger density, are filtered out, thereby improving detection accuracy.
  • the detection recipe setting and optimization method also includes:
  • S500 Perform defect analysis on the object to be detected according to the detection formula and the values of the detection parameters of the detection formula, and obtain defect data information of the object to be detected.
  • Figure 13 schematically shows a comparison diagram of the detection result data obtained by the detection process using the detection recipe setting and optimization method proposed by the present invention and the detection result data obtained by the original detection process. It can be seen from Figure 13 that by applying the strategy and parameter setting values of the detection process obtained by reverse derivation of the present invention for the detection process, the nuisance noise data is filtered out, the true defect data (defect defect data) is retained, and the detection result data is passed The distribution in the feature space can visually test the correctness of the results.
  • the first data sample includes several pieces of detection result data, and the detection result data includes a large amount of auxiliary parameter adjustment information, which can be effectively used for subsequent use through data annotation.
  • Historical information can be used for data analysis and reasoning to obtain accurate prior knowledge, which provides an important basis and can improve the detection accuracy of detection formulas.
  • the detection recipe strategy and parameter setting values are obtained through reverse derivation based on the defect distribution boundary information and the preset outlier statistical analysis strategy. Therefore, the present invention can simultaneously deduce a set of detection parameters (adjusting all parameters at the same time) through reverse derivation.
  • the coupling relationship between parameters is also taken into account, realizing rapid modeling of the detection process; avoiding repeated adjustment of parameters. , which can significantly save labor and time costs; moreover, for new process defect detection, users can determine the strategy and parameter setting values of the detection process without the need for algorithm foundation.
  • FIG. 14 schematically provides a structural block diagram of the detection recipe setting and optimization device provided by this embodiment.
  • the detection recipe setting and optimization device provided by this embodiment includes: a true defect and noise marking unit 100, a feature distribution information acquisition unit 200, a defect distribution boundary acquisition unit 300, and a detection parameter setting and optimization unit. 400.
  • the true defect and noise marking unit 100 is configured to mark a first data sample to obtain a second data sample; wherein the first data sample includes several pieces of detection result data; and the second The data sample includes the detection result data and the label corresponding to each piece of the detection result data.
  • the feature distribution information acquisition unit 200 is configured to obtain data feature distribution information of the detection object based on the second data sample.
  • the defect distribution boundary acquisition unit 300 is configured to use a preset outlier statistical analysis strategy to perform outlier statistical analysis on the data feature distribution information, obtain defect distribution boundary information, and use it to perform outlier statistical analysis according to the preset outlier statistics. Analyze strategies and determine detection recipes.
  • the detection parameter setting and optimization unit 400 is configured to set or optimize the values of detection parameters of the detection formula through reverse derivation based on the defect distribution boundary information and the preset outlier statistical analysis strategy.
  • the detection recipe setting and optimization device further includes a detection recipe application unit 500 .
  • the detection recipe application unit 500 is configured to perform defect analysis on the object to be detected according to the detection recipe and the values of detection parameters of the detection recipe, and obtain defect data information of the object to be detected.
  • the detection recipe setting and optimization device provided by the present invention Since the basic principles of the detection recipe setting and optimization device provided by the present invention are similar to the detection recipe setting and optimization methods provided by the above embodiments, in order to avoid redundancy, the specific content of the above detection recipe setting and optimization device implementation is introduced. It is relatively rough. For detailed information, please refer to the detailed description of the detection recipe settings and optimization methods above. Furthermore, since the detection recipe setting and optimization device provided by the present invention and the detection recipe setting and optimization method provided by the above embodiments belong to the same inventive concept, the detection recipe setting and optimization device provided by the present invention at least has the same features as the detection recipe setting and optimization method. The recipe setting and optimization method have the same beneficial effects. You can refer to the relevant content in the detection recipe setting and optimization method above, so this will not be described again.
  • the detection formula setting and optimization device in the present invention and the detection formula setting and optimization method described above belong to the same inventive concept, the introduction to the detection formula setting and optimization device in this article is relatively simple. Regarding how, you can Refer to the detection recipe settings above It is related to the optimization method, so it will not be described again.
  • the present invention also provides an electronic device.
  • FIG. 15 schematically shows a block structure diagram of the electronic device provided by an embodiment of the present invention.
  • the electronic device includes a processor 601 and a memory 603.
  • a computer program is stored on the memory 603.
  • the detection recipe settings described above are implemented. and optimization methods. Since the electronic device provided by the present invention and the detection recipe setting and optimization method described above belong to the same inventive concept, it has all the advantages of the detection recipe setting and optimization method described above, and thus will not be described again.
  • the electronic device also includes a communication interface 602 and a communication bus 604 , wherein the processor 601 , the communication interface 602 , and the memory 603 complete communication with each other through the communication bus 604 .
  • the communication bus 604 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc.
  • PCI Peripheral Component Interconnect
  • EISA Extended Industry Standard Architecture
  • the communication bus 604 can be divided into an address bus, a data bus, a control bus, etc. For ease of presentation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
  • the communication interface 602 is used for communication between the above-mentioned electronic device and other devices.
  • the processor 601 referred to in the present invention can be a central processing unit (Central Processing Unit, CPU), or other general-purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general processor may be a microprocessor or the processor may be any conventional processor, etc.
  • the processor 601 is the control center of the electronic device and uses various interfaces and lines to connect various parts of the entire electronic device.
  • the memory 603 can be used to store the computer program.
  • the processor 601 implements various functions of the electronic device by running or executing the computer program stored in the memory 603 and calling the data stored in the memory 603. Function.
  • the memory 603 may include non-volatile and/or volatile memory.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • the present invention also provides a readable storage medium.
  • a computer program is stored in the readable storage medium.
  • the computer program is executed by a processor, the above-mentioned detection recipe setting and optimization method can be implemented. Since the readable storage medium provided by the present invention and the detection recipe setting and optimization method described above belong to the same inventive concept, it has all the advantages of the detection recipe setting and optimization method described above, so this will not be discussed further. Repeat.
  • the readable storage medium in the embodiment of the present invention may be any combination of one or more computer-readable media.
  • the readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination thereof.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in combination with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device .
  • Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional Procedural programming language - such as "C" or similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as an Internet service provider) through the Internet. ).
  • LAN local area network
  • WAN wide area network
  • Internet service provider such as an Internet service provider
  • the detection recipe setting and optimization method, device, electronic equipment and storage medium provided by the present invention have the following advantages: the first data sample includes several pieces of detection result data, and the detection result data is
  • the result data includes auxiliary parameter adjustment information (such as the basic information and characteristic data information of the detection object, the characteristic data information includes but is not limited to the grayscale, shape, texture and other information of the defects indicated by the detection results), through data annotation It can distinguish true defect data from noise data, which provides an important basis for subsequent effective use of historical information for data analysis and reasoning to obtain accurate prior knowledge, and can improve the detection accuracy of detection formulas.
  • the detection recipe strategy and detection parameter values are obtained through reverse derivation based on the defect distribution boundary information and the preset outlier statistical analysis strategy. Therefore, the present invention can deduce a set of detection parameters at the same time (adjusting all parameters at the same time) through reverse derivation.
  • the coupling relationship between parameters is also taken into account, realizing rapid modeling of detection formulas; avoiding repeated adjustment of parameters. , which can significantly save labor and time costs; moreover, for new process defect detection, the user can determine the strategy of the detection formula and the values of the detection parameters of the detection formula without having any algorithm foundation.
  • each block in the flowchart or block diagrams may represent a module, program, or portion of code that contains one or more operable functions for implementing the specified logical functions.
  • Execution instructions, the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical function.
  • the functions noted in the block may occur out of the order noted in the figures.
  • each block in the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration can be designed into specialized hardware-based systems that perform the specified functions or acts. Implemented, or may be implemented using a combination of dedicated hardware and computer instructions.
  • each functional module in each embodiment of this article can be integrated together to form an independent part, each module can exist alone, or two or more modules can be integrated to form an independent part.

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Abstract

La présente invention concerne un procédé et un appareil de configuration et d'optimisation de formule de détection, un dispositif électronique et un support de stockage. Le procédé consiste à : étiqueter un premier échantillon de données pour obtenir un second échantillon de données ; le premier échantillon de données comprenant une pluralité d'éléments de données de résultat de détection et le second échantillon de données comprenant les données de résultat de détection et une étiquette correspondant à chaque élément de données ; selon le second échantillon de données, obtenir des informations de distribution de caractéristiques de données d'un objet de détection ; utiliser une stratégie d'analyse statistique aberrante prédéfinie, effectuer une analyse statistique aberrante sur les informations de distribution de caractéristiques de données, de façon à obtenir des informations de limite de distribution de défauts et déterminer une formule de détection ; enfin, selon les informations de limite de distribution de défauts et la stratégie d'analyse statistique aberrante prédéfinie, déterminer ou optimiser les valeurs des paramètres de détection de la formule de détection au moyen d'une dérivation inverse. Dans l'invention, la relation de couplage entre les paramètres est considérée, de sorte qu'un ajustement répété des paramètres peut être évité et, pendant ce temps, un ensemble entier de paramètres de détection sont déduits, de façon à obtenir une modélisation rapide de la formule de détection ; et des coûts de main-d'œuvre et de temps peuvent être économisés.
PCT/CN2023/091070 2022-04-29 2023-04-27 Procédé et appareil de configuration et d'optimisation de formule de détection, dispositif électronique et support de stockage WO2023208091A1 (fr)

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