Data analysis method and device, electronic device, and storage medium

Abstract

The present disclosure provides a data analysis method and device, an electronic device, and a storage medium, which relate to the field of semiconductor technology. The data analysis method includes: obtaining a target yield problem stacking graph corresponding to a wafer group, and obtaining a measurement data stacking graph of the wafer group under different types of tests; performing graphic matching between the target yield problem stacking graph and the measurement data stacking graph to obtain matching data; calculating the correlation data corresponding to the measurement data stacking graph and the target yield problem stacking graph; performing weighted calculation on the matching data and the correlation data, and determining the target measurement parameters that cause the target yield problem according to the weighted calculation result. The technical solution of the embodiment of the present disclosure can improve the efficiency of screening and the accuracy of the determined target measurement parameters when screening the target measurement parameters that cause the target yield problem.

Description

Background Art

[0002] With the rapid development of science and technology, semiconductor devices (Semiconductor Device), as an electronic device with conductivity between good conductors and insulators, use the special electrical properties of semiconductor materials to complete specific functions. Because they can be used to generate, control, receive, transform, amplify signals and perform energy conversion and other functions, they are increasingly valued and concerned by people.

[0003] Wafer refers to the silicon chip used to make silicon semiconductor integrated circuits. Due to the influence of the process of processing the wafer, the yield of the generated wafer is sometimes high and sometimes low. Therefore, it is necessary to analyze the yield problem that causes the wafer yield, so as to adjust the process according to the results of the yield problem analysis and improve the wafer yield.

[0004] Currently, when analyzing process parameters that cause wafer yield problems, conclusions are generally drawn through manual analysis and comparison. However, this solution makes the analysis efficiency of yield problems low, the analysis cycle long, and the accuracy of the analysis results obtained by manual analysis is low.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The purpose of the embodiments of the present disclosure is to provide a data analysis method, a data analysis device, an electronic device, and a computer-readable storage medium, thereby overcoming, at least to a certain extent, the problem of low efficiency and low accuracy in screening target measurement parameters that lead to target yield problems in related schemes.

[0007] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0008] According to a first aspect of an embodiment of the present disclosure, there is provided a data analysis method, comprising:

[0009] Obtaining a target yield problem stacking graph corresponding to a wafer group having a target yield problem, and obtaining a measurement data stacking graph of the wafer group under different types of tests;

[0010] Performing graphic matching on the target yield problem stacking graph and each of the measurement data stacking graphs to obtain matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs;

[0011] Calculating correlation data corresponding to each of the measurement data stacking graphs and the target yield problem stacking graph;

[0012] A weighted calculation is performed on the matching degree data and the correlation data, and a target measurement parameter causing the target yield problem is determined according to the weighted calculation result.

[0013] In some example embodiments of the present disclosure, based on the above solution, obtaining a target yield problem stacking pattern corresponding to a wafer group having a target yield problem includes:

[0014] Acquire a wafer group having a target yield problem, each wafer in the wafer group having a target yield problem distribution pattern;

[0015] The target yield problem distribution graphs corresponding to the wafers are stacked to obtain a target yield problem stacking graph.

[0016] In some example embodiments of the present disclosure, based on the above solution, obtaining a stacked graph of measurement data of the wafer group under different types of tests includes:

[0017] Acquire measurement data sets of each of the wafers in the wafer group under different types of tests;

[0018] Generating a distribution graph of each of the measurement data based on the measurement data set;

[0019] The measurement data distribution graphs are stacked to obtain the wafer group and the measurement data stacking graphs.

[0020] In some example embodiments of the present disclosure, based on the above scheme, the target yield problem stacking graph is graphically matched with each of the measurement data stacking graphs to generate matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs, including:

[0021] Extracting a first graphic feature in the target yield problem stacked graph, and extracting a second graphic feature in each of the measurement data stacked graphs;

[0022] Calculating a first eigenvector corresponding to the first graphic feature and a second eigenvector corresponding to the second graphic feature;

[0023] Matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs is calculated according to the first feature vector and the second feature vector.

[0024] In some example embodiments of the present disclosure, based on the above solution, the matching data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs are calculated according to the first feature vector and the second feature vector, including:

[0025] Calculating similarity data between the first feature vector and the second feature vector;

[0026] The similarity data is used as matching data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs.

[0027] In some example embodiments of the present disclosure, based on the above scheme, calculating the correlation data corresponding to each of the measurement data stacking patterns and the target yield problem stacking pattern includes:

[0028] Partitioning the target yield problem stacked graph and the measurement data stacked graph to determine a main graph area;

[0029] Correlation data between the target yield problem stacked pattern and each of the measurement data stacked patterns is calculated based on the main pattern area.

[0030] In some example embodiments of the present disclosure, based on the above solution, calculating the correlation data between each of the measurement data stacking patterns and the target yield problem stacking pattern based on the main pattern area includes:

[0031] Determine wafer yield data corresponding to the main graphic area in the target yield problem stacking graphic;

[0032] Determine the statistical characteristic data of the measurement parameter corresponding to the main graphic area in each of the measurement data stacked graphics;

[0033] The correlation data between the target yield problem stacking graph and each of the measurement data stacking graphs is calculated using the wafer yield data and the statistical feature data in the main graph area.

[0034] In some example embodiments of the present disclosure, based on the above solution, determining the target measurement parameter causing the target yield problem according to the weighted calculation result includes:

[0035] sorting the measurement data stacked graphs according to the weighted calculation results, and determining the measurement data stacked graph corresponding to the largest weighted calculation result as the most relevant measurement data stacked graph;

[0036] The measurement parameter corresponding to the most relevant measurement data stacking pattern is used as the target measurement parameter causing the target yield problem.

[0037] In some example embodiments of the present disclosure, based on the above solution, after obtaining the most relevant measurement data stacking graph and taking the measurement parameter corresponding to the most relevant measurement data stacking graph as the target measurement parameter, the method further includes:

[0038] According to the matching degree data and the correlation data, respectively determining a measurement data stacking pattern having the highest matching degree and a measurement data stacking pattern having the highest correlation with the target yield problem stacking pattern;

[0039] Determine the highest matching measurement parameter and the highest correlation measurement parameter corresponding to the target yield problem stacking graph based on the measurement data stacking graph with the highest matching degree and the measurement data stacking graph with the highest correlation;

[0040] A rationality judgment process is performed on the highest matching measurement parameter, the highest correlation measurement parameter, the target measurement parameter and the target yield problem to determine the measurement parameter most relevant to the target yield problem according to the rationality judgment result.

[0041] In some example embodiments of the present disclosure, based on the above scheme, weighted calculation is performed on the matching degree data and the correlation data, including:

[0042] The matching degree data and the correlation data are weightedly calculated by a pre-selected target weighted calculation method.

[0043] In some example embodiments of the present disclosure, based on the above solution, before performing weighted calculation on the matching degree data and the correlation data, the method further includes:

[0044] Obtaining a sample yield problem stacking graph and a sample measurement data stacking graph pre-stored in a database; wherein the database also includes sample measurement parameters of the yield problem that causes the sample yield problem stacking graph;

[0045] Get multiple pre-set weighted calculation methods;

[0046] The sample yield problem stacking graph and the sample matching degree data and the sample correlation data corresponding to the sample measurement data stacking graph are weighted and calculated in sequence according to a plurality of weighted calculation methods;

[0047] A target weighted calculation method is determined from the multiple weighted calculation methods according to the weighted calculation results corresponding to the multiple weighted calculation methods and the sample measurement parameters.

[0048] In some example embodiments of the present disclosure, based on the aforementioned solution, the measurement parameters include one or more combinations of defect parameters, electrical parameters and structural parameters.

[0049] According to a second aspect of an embodiment of the present disclosure, there is provided a data analysis device, including:

[0050] A pattern acquisition module, used to acquire a target yield problem stacking pattern corresponding to a wafer group having a target yield problem, and to acquire a measurement data stacking pattern of the wafer group under different types of tests;

[0051] A correlation degree calculation module, used to calculate the matching degree data and the correlation data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs, and perform weighted calculation on the matching degree data and the correlation data to obtain a weighted calculation result;

[0052] The target measurement parameter analysis module is used to analyze the weighted calculation result to determine the target measurement parameter that causes the target yield problem.

[0053] In an exemplary embodiment of the present disclosure, based on the above solution, the association degree calculation module further includes:

[0054] A matching degree data calculation unit, used for performing graphic matching between the target yield problem stacking graph and the measurement data stacking graph to obtain matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs;

[0055] The correlation data calculation unit is used to calculate the correlation data corresponding to each of the measurement data stacking patterns and the target yield problem stacking pattern.

[0056] In an exemplary embodiment of the present disclosure, based on the above solution, the correlation data calculation unit further includes:

[0057] A graphic partitioning subunit, used to partition the target yield problem stacked graphic and the measurement data stacked graphic to determine a main graphic area;

[0058] A wafer yield determination subunit, used to determine wafer yield data corresponding to the main graphic area in the target yield problem stacking graphic;

[0059] A statistical feature determination subunit, used to determine the statistical feature data of the measurement parameter corresponding to the main graphic area in each of the measurement data stacked graphics;

[0060] The correlation data calculation subunit is used to calculate the correlation data between the target yield problem stacking graph and each of the measurement data stacking graphs through the wafer yield data and the statistical feature data in the main graph area.

[0061] In an exemplary embodiment of the present disclosure, based on the above solution, the graph acquisition module further includes a target yield problem stacking graph acquisition unit, and the target yield problem stacking graph acquisition unit is configured as follows:

[0062] Acquire a wafer group having a target yield problem, each wafer in the wafer group having a target yield problem distribution pattern;

[0063] The target yield problem distribution graphs corresponding to the wafers are stacked to obtain a target yield problem stacking graph.

[0064] In an exemplary embodiment of the present disclosure, based on the above solution, the graphic acquisition module further includes a measurement data stacking graphic acquisition unit, and the measurement data stacking graphic acquisition unit is configured as follows:

[0065] Acquire measurement data sets of each of the wafers in the wafer group under different types of tests;

[0066] Generating a distribution graph of each of the measurement data based on the measurement data set;

[0067] The measurement data distribution graphs are stacked to obtain the wafer group and the measurement data stacking graphs.

[0068] In an exemplary embodiment of the present disclosure, based on the above solution, the matching degree data calculation unit further includes:

[0069] A graphic feature extraction subunit, used to extract a first graphic feature in the target yield problem stacked graphic, and to extract a second graphic feature in each of the measurement data stacked graphics;

[0070] a feature vector calculation subunit, configured to calculate a first feature vector corresponding to the first graphic feature and a second feature vector corresponding to the second graphic feature;

[0071] The matching degree calculation subunit is used to calculate the matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs according to the first feature vector and the second feature vector.

[0072] In an exemplary embodiment of the present disclosure, based on the above solution, the matching degree calculation subunit is further configured as follows:

[0073] Calculating similarity data between the first feature vector and the second feature vector;

[0074] The similarity data is used as matching data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs.

[0075] In an exemplary embodiment of the present disclosure, based on the above solution, the target measurement parameter analysis module further includes a first analysis unit, and the first analysis unit is configured to:

[0076] sorting the measurement data stacked graphs according to the weighted calculation results, and taking the measurement data stacked graph corresponding to the largest weighted calculation result as the most relevant measurement data stacked graph;

[0077] The measurement parameter corresponding to the most relevant measurement data stacking pattern is used as the target measurement parameter causing the target yield problem.

[0078] In an exemplary embodiment of the present disclosure, based on the above solution, the target measurement parameter analysis module further includes a second analysis unit, and the second analysis unit is configured as follows:

[0079] According to the matching degree data and the correlation data, respectively determining a measurement data stacking pattern having the highest matching degree and a measurement data stacking pattern having the highest correlation with the target yield problem stacking pattern;

[0080] Determine the highest matching measurement parameter and the highest correlation measurement parameter corresponding to the target yield problem stacking graph based on the measurement data stacking graph with the highest matching degree and the measurement data stacking graph with the highest correlation;

[0081] A rationality judgment process is performed on the highest matching measurement parameter, the highest correlation measurement parameter, the target measurement parameter and the target yield problem to determine the measurement parameter most relevant to the target yield problem according to the rationality judgment result.

[0082] In an exemplary embodiment of the present disclosure, based on the above solution, the data analysis device further includes a weighted calculation method screening unit, and the weighted calculation method screening unit is configured to:

[0083] Obtaining a sample yield problem stacking graph and a sample measurement data stacking graph pre-stored in a database; wherein the database also includes sample measurement parameters of the yield problem that causes the sample yield problem stacking graph;

[0084] Get multiple pre-set weighted calculation methods;

[0085] The sample yield problem stacking graph and the sample matching degree data and the sample correlation data corresponding to the sample measurement data stacking graph are weighted and calculated in sequence according to a plurality of weighted calculation methods;

[0086] A target weighted calculation method is determined from the multiple weighted calculation methods according to the weighted calculation results corresponding to the multiple weighted calculation methods and the sample measurement parameters.

[0087] In an exemplary embodiment of the present disclosure, based on the above-mentioned solution, the target measurement parameters include one or more combinations of defect parameters, electrical parameters and structural parameters.

[0088] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and wherein the computer-readable instructions, when executed by the processor, implement any one of the above-described data analysis methods.

[0089] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the data analysis method according to any one of the above items is implemented.

[0090] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0091] The data analysis method in the exemplary embodiment of the present disclosure performs graphic matching on the target yield problem stacking graph corresponding to the wafer group with the target yield problem and the measurement data stacking graph under different types of tests of the wafer group, determines the matching degree data of the target yield problem stacking graph and the measurement data stacking graph; calculates the correlation data of the measurement data stacking graph and the target yield problem stacking graph; performs weighted calculation on the matching degree data and the correlation data, and determines the target measurement parameters that cause the target yield problem according to the weighted calculation result. On the one hand, the target yield problem stacking graph is automatically matched with each measurement data stacking graph, and the target measurement parameters that cause the target yield problem of the wafer group are automatically determined according to the matching degree data and correlation data obtained by the graphic matching, without the need for manual analysis, which effectively improves the analysis efficiency of the target measurement parameters that cause the target yield problem; on the other hand, the target measurement parameters are determined by combining the matching degree data and correlation data of the target yield problem stacking graph with each measurement data stacking graph, which can improve the analysis efficiency while ensuring the accuracy of the analysis results, and effectively improve product quality.

[0092] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0094] Figure 1 The following is a schematic diagram showing a flow chart of a data analysis method according to an embodiment of the present disclosure;

[0095] Figure 2 A schematic diagram of a process for generating a target yield problem stacking graph according to an embodiment of the present disclosure is schematically shown;

[0096] Figure 3 A schematic diagram of a process for calculating matching degree data according to an embodiment of the present disclosure is shown;

[0097] Figure 4 A schematic diagram of a process for calculating correlation data according to an embodiment of the present disclosure is shown;

[0098] Figure 5 A schematic diagram of a process for determining a target weighted calculation method according to an embodiment of the present disclosure is shown;

[0099] Figure 6 The following schematically shows an application diagram of determining target measurement parameters according to an embodiment of the present disclosure;

[0100] Figure 7 A schematic diagram of a data analysis device according to an embodiment of the present disclosure is schematically shown;

[0101] Figure 8 A schematic diagram schematically shows the structure of a computer system of an electronic device according to an embodiment of the present disclosure;

[0102] Fig. 9 A schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure is schematically shown.

[0103] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0104] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0105] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0106] Furthermore, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0107] In this example embodiment, a data analysis method is first provided. The data analysis method can be applied to a terminal device or a server. This example embodiment does not specifically limit this. The following description will take the execution of the method by a server as an example. Figure 1 A schematic diagram schematically shows the data analysis method flow according to some embodiments of the present disclosure, referring to Figure 1 As shown, the data analysis method may include the following steps:

[0108] Step S110, obtaining a target yield problem stacking graph corresponding to a wafer group having a target yield problem, and obtaining a measurement data stacking graph of the wafer group under different types of tests;

[0109] Step S120, performing graphic matching on the target yield problem stacking graph and each of the measurement data stacking graphs to obtain matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs;

[0110] Step S130, calculating correlation data corresponding to each of the measurement data stacking patterns and the target yield problem stacking pattern;

[0111] Step S140 , performing weighted calculation on the matching degree data and the correlation data, and determining the target measurement parameter causing the target yield problem according to the weighted calculation result.

[0112] According to the data analysis method in this example embodiment, on the one hand, the target yield problem stacking graph is automatically matched with each measurement data stacking graph, and the target measurement parameters that cause the target yield problem of the wafer group are automatically determined based on the matching data and correlation data obtained by the graphic matching. There is no need for manual analysis, which effectively improves the analysis efficiency of the target measurement parameters that cause the target yield problem. On the other hand, the target measurement parameters are determined in combination with the matching data and correlation data of the target yield problem stacking graph and each measurement data stacking graph, which can improve the analysis efficiency while ensuring the accuracy of the analysis results, thereby effectively improving product quality.

[0113] Next, the data analysis method in this example embodiment will be further described.

[0114] In step S110 , a target yield problem stacking graph corresponding to a wafer group having a target yield problem is obtained, and a measurement data stacking graph of the wafer group under different types of tests is obtained.

[0115] In an example embodiment of the present disclosure, the yield may be the wafer yield (Wafer Yield), which refers to the ratio of the number of chips that have passed the test after completing all process steps to the effective chips on the entire wafer. The yield problem may be various process problems that cause the wafer yield to fail to meet the standard yield requirements. For example, the yield problem may be caused by a defect problem (Defect), an electrical problem (WAT), or a structural problem (MET). Of course, it may also be other problems in various process steps that cause the wafer yield to fail to meet the standard yield requirements. This example embodiment does not specifically limit this.

[0116] Specifically, the defect parameters corresponding to the defect problem may include measurement parameters such as line width parameters, depth parameters, film thickness parameters, uniformity parameters, height difference parameters, curvature parameters, etc. in each process; the electrical parameters corresponding to the electrical problem may include measurement parameters such as on-current parameters, isotropic leakage current parameters, threshold voltage parameters, breakdown voltage parameters, capacitance parameters, inductance parameters, resistance parameters, etc. between various structures of various semiconductor components; the structural parameters corresponding to the structural problem may include structural abnormality parameters that appear in each process. Of course, this is only a schematic example, and the present example embodiment is not limited to this.

[0117] In an example embodiment of the present disclosure, step S110 may include steps S210 to S220, and a target yield problem stacking graph may be generated through steps S210 to S220:

[0118] refer to Figure 2 As shown, step S210, obtaining a wafer group with a target yield problem, each wafer in the wafer group has a target yield problem distribution graph;

[0119] Step S220, stacking the target yield problem distribution graphs corresponding to the wafers to obtain a target yield problem stacking graph.

[0120] Among them, a wafer group (Lot / Wafer list) may refer to a collection of wafers having the same target yield problem. The wafer group may include a target yield problem distribution graph of each wafer. The target yield problem distribution graph may be a feature graph containing various measurement parameters having the same target yield problem obtained by a user in real time through an input interface, or may be a feature graph containing various measurement parameters having the same target yield problem under process flows of different time periods collected and stored in advance. Of course, it may also be a feature graph containing various measurement parameters having the same target yield problem obtained in other ways, and this example embodiment does not make any special limitation on this.

[0121] The target yield problem stacked graph may refer to a graph containing the target yield problem distribution characteristics obtained by stacking the target yield problem distribution graphs corresponding to each wafer in the wafer group. By superimposing the target yield problem distribution graphs with the target yield problem, the graphic characteristics corresponding to the target yield problem are obtained, so as to facilitate the subsequent automatic graphic feature matching of the target yield problem stacked graph.

[0122] In an exemplary embodiment of the present disclosure, the measurement data stacking graph may also be generated by the following steps:

[0123] Acquire measurement data sets of each of the wafers in the wafer group under different types of tests;

[0124] Generating measurement data distribution graphs corresponding to different types of tests based on the measurement data set;

[0125] The measurement data distribution graphs are stacked to obtain a measurement data stacking graph corresponding to the wafer group.

[0126] Among them, the measurement parameters may refer to parameters corresponding to various process problems that may cause the target yield problems of the wafer group under different types of tests. For example, the measurement parameters may be defect parameters corresponding to defect problems, electrical parameters corresponding to electrical problems, or structural parameters corresponding to structural problems. Of course, they may also be measurement parameters corresponding to various other process problems that may cause the target yield problems of the wafer group. This example embodiment does not make any special limitations on this.

[0127] A measurement data set may refer to a collection of measurement data corresponding to each wafer in a wafer group under different types of tests. Through the measurement data set, a measurement data distribution graph corresponding to each measurement parameter may be generated, and then the measurement data distribution graph corresponding to each measurement parameter may be stacked to obtain multiple measurement data stacking graphs of the wafer group under different types of tests.

[0128] For example, there are many types of yield tests for wafer groups, such as DC test, functional test, AC test, etc., among which DC test verifies voltage and current parameters; functional test verifies the correctness of a series of logical function operations inside the chip, etc. There are many subcategories of yield under these categories, which are generally expressed by Bin, and different types of Bin represent different test types. For example, symbols such as "!", "@", "#", "$", "%, "&", "[", "+" can be used to mark the die that fails the DC parameter test (DC fail, current cannot enter), symbols such as "a", "F" can be used to mark the die that completely fails the read and write functions, and symbols such as "X", "Y", "s" can be used to mark the die that has a slow read and write speed function (Margin fail), etc. Of course, this is only a schematic example, and the numbers are different due to the different use of semiconductor manufacturers, which should not cause any special limitations on this exemplary embodiment. In addition to Bin, there is also Failure Shape Analysis (FSA), which also has corresponding test item numbers. That is to say, different Bins or different FSAs represent different failure causes. During the yield test, generally a wafer will get a Map graph, which reflects the failure status of each grain.

[0129] The purpose of this disclosure is to obtain a wafer yield problem graph, and then how to associate the yield problem with the process problem corresponding to the wafer group, and the process problem is reflected through measurement data. After each process flow is completed, a variety of measurements will be performed, and these measurement data are indicators that reflect the quality of the process. In each measurement process, a corresponding Map graph will also be obtained. However, since there are many kinds of process problems that lead to yield problems, the technical purpose of this disclosure is to find a certain measurement graph that has the greatest impact on the yield problem of the wafer group through graph matching and graph correlation judgment, and then locate the process that is most related to the yield problem of the wafer group from this measurement graph, and then adjust the process steps to improve the yield of the wafer and improve product quality.

[0130] In step S120, the target yield problem stacking graph is matched with each of the measurement data stacking graphs to obtain matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs.

[0131] In an example embodiment of the present disclosure, the matching degree data may refer to data characterizing the degree of matching between the graphic features in the measurement data stacking graph and the target yield problem stacking graph. For example, the matching degree data (between 0.0-1.0 or 0%-100%) may be 0.1 or 10%, or 0.9 or 90%. Of course, it may also be other data that can characterize the degree of matching between the graphic features in the measurement data stacking graph and the target yield problem stacking graph. This example embodiment does not make any special limitation on this.

[0132] Specifically, a target measurement data stacking graph corresponding to the measurement parameters under different types of input tests can be determined in the measurement data stacking graph, and then the target yield problem stacking graph can be graphically matched with the target measurement data stacking graph corresponding to the measurement parameters.

[0133] Among them, the target measurement data stacking graph may refer to the measurement data stacking graph corresponding to the measurement parameters input in the measurement data stacking graph. First, based on the measurement parameters under different types of input tests, the measurement data distribution graphs corresponding to the wafer group under the measurement parameters are stacked to obtain the measurement data stacking graph, and then the target measurement data stacking graph corresponding to the measurement parameters is matched with the target yield problem stacking graph.

[0134] In an exemplary embodiment of the present disclosure, step S120 may include steps S310 to S330, and matching degree data may be calculated through steps S310 to S330:

[0135] refer to Figure 3 As shown, step S310, extracting a first graphic feature in the target yield problem stacked graph, and extracting a second graphic feature in each of the measurement data stacked graphs;

[0136] Step S320, calculating a first eigenvector corresponding to the first graphic feature and a second eigenvector corresponding to the second graphic feature;

[0137] Step S330 , obtaining matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs according to the first feature vector and the second feature vector.

[0138] Among them, the first graphic feature can be a graphic feature corresponding to the target yield problem distribution in the target yield problem stacking graph, and the second graphic feature can be a graphic feature corresponding to different measurement parameter distributions in the measurement data stacking graph. For example, the first graphic feature in the target yield problem stacking graph and the second graphic feature in the measurement data stacking graph corresponding to each measurement parameter can be extracted through a pre-built convolutional neural network model, or the first graphic feature in the target yield problem stacking graph and the second graphic feature in the measurement data stacking graph can be extracted through a scale-invariant feature transform (SIFT) algorithm, or the first graphic feature in the target yield problem stacking graph and the second graphic feature in the measurement data stacking graph can be extracted through a histogram of oriented gradient (HOG) algorithm. Of course, other methods that can extract graphic features are also possible, and this example embodiment does not specifically limit this.

[0139] After extracting the first graphic feature in the target yield problem stacking graph and the second graphic feature in the target measurement data stacking graph, the first feature vector corresponding to the first graphic feature and the second feature vector corresponding to the second graphic feature can be calculated, and then the matching data between the target yield problem stacking graph and the measurement data stacking graph can be determined based on the first feature vector and the second feature vector.

[0140] It should be noted that the “first” and “second” in this example embodiment are only used to distinguish different graphic features and feature vectors corresponding to different graphic features, and have no special meaning here and should not cause any special limitation to this example embodiment.

[0141] Furthermore, the matching data can be calculated based on the feature vector by the following steps:

[0142] Calculating similarity data between the first feature vector and the second feature vector;

[0143] The similarity data is used as matching data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs.

[0144] Among them, the similarity data may refer to data representing the first eigenvector and the second eigenvector. For example, the similarity data may be cosine similarity data (CosineSimilarity) corresponding to the first eigenvector and the second eigenvector, or may be the Pearson Correlation Coefficient (Pearson Correlation Coefficient) corresponding to the first eigenvector and the second eigenvector, or may be the Euclidean Distance (Euclidean Distance) corresponding to the first eigenvector and the second eigenvector. Of course, it may also be other data that can represent the first eigenvector and the second eigenvector, and this example embodiment does not make any special limitation on this.

[0145] Since the first eigenvector and the second eigenvector are eigenvectors corresponding to the graphical features of the target yield problem distribution in the target yield problem stacking graph and the graphical features of the different measurement parameter distributions in the measurement data stacking graph, respectively, the similarity data between the first eigenvector and the second eigenvector can be used as data characterizing the degree of matching between the target yield problem stacking graph and the measurement data stacking graph.

[0146] Of course, the matching data of the target yield problem stacking graph and each measurement data stacking graph can also be obtained by other means. For example, the matching data of the target yield problem stacking graph and the target measurement data stacking graph can be determined by a trained deep learning model or a random forest model. This example embodiment is not limited to this.

[0147] In step S130 , correlation data corresponding to each of the measurement data stacking patterns and the target yield problem stacking pattern is calculated.

[0148] In an example embodiment of the present disclosure, correlation data may refer to a quantity that can characterize the degree of correlation between the measurement data stacking graph and the target yield problem stacking graph. For example, the chip yield in the main graphic area of ​​the target yield problem stacking graph may be used as the horizontal axis of the correlation coordinate system, and the average value of the measurement parameter corresponding to each chip in the main graphic area of ​​the measurement data stacking graph may be used as the vertical axis of the correlation coordinate system, and the correlation data between the measurement data stacking graph and the target yield problem stacking graph may be calculated based on the curve in the obtained correlation coordinate system. Of course, other methods that can characterize the correlation data between the measurement data stacking graph and the target yield problem stacking graph may also be used, and this example embodiment does not specifically limit this.

[0149] Specifically, step S130 may include steps S410 to S420. Before calculating the correlation data, the measurement data stacking graph and the target yield problem stacking graph may be partitioned by steps S410 to S420:

[0150] refer to Figure 4 As shown, step S410, partitioning the target yield problem stacking graph and the measurement data stacking graph to determine a main graph area;

[0151] Step S420 , calculating correlation data between the target yield problem stacked pattern and each of the measurement data stacked patterns based on the main pattern area.

[0152] Among them, partitioning can refer to the process of dividing the area according to the distribution of graphic features in the target yield problem stacking graph or the measurement data stacking graph. For example, the target yield problem stacking graph or the measurement data stacking graph can be partitioned according to a fixed-size graphic area (such as a 10*10 graphic area), or the target yield problem stacking graph or the measurement data stacking graph can be partitioned according to the density of the graphic feature distribution, or the entire target yield problem stacking graph or the measurement data stacking graph can be selected for partitioning, and the partitioning is specifically performed according to the distribution of graphic features of the target yield problem stacking graph or the measurement data stacking graph in actual conditions. This example embodiment does not make special restrictions on this. The main graphic area can refer to the main feature distribution area formed after partitioning the target yield problem stacking graph or the measurement data stacking graph. Preferably, the main graphic area in the target yield problem stacking graph can correspond to the main graphic area in the measurement data stacking graph (such as the shape of the graphic area, the position of the graphic area, the size of the graphic area, etc.).

[0153] Preferably, the correlation data corresponding to the measurement data stacking graph and the target yield problem stacking graph can be calculated by the following steps: the wafer yield data corresponding to the main graphic area in the target yield problem stacking graph can be determined; the statistical characteristic data of the measurement parameters corresponding to the main graphic area in each measurement data stacking graph can be determined; the correlation data between the target yield problem stacking graph and each measurement data stacking graph is calculated through the wafer yield data in the main graphic area and the statistical characteristic data.

[0154] For example, assuming that the graphic features of the target yield problem stacking graph are mainly concentrated in a certain graphic area (i.e., the main graphic area), and there are a total of 100 chip dies in the main graphic area, of which 10 chip dies fail, then the yield of the graphic area is 90%. Therefore, the yield value corresponding to the graphic area can be used as the vertical coordinate of the correlation curve, and then the average value of the WAT A values ​​in the measurement data stacking graph corresponding to the WAT A parameter (i.e., the measurement parameter at this time is the WAT A parameter) of the selected 100 chip dies is used as the horizontal coordinate of the correlation curve, and then the correlation data between the measurement data stacking graph and the target yield problem stacking graph is determined based on the constructed correlation curve.

[0155] In step S140, weighted calculation is performed on the matching degree data and the correlation data, and the target measurement parameter causing the target yield problem is determined according to the weighted calculation result.

[0156] In an example embodiment of the present disclosure, the target measurement parameter may refer to a parameter that is obtained after analysis and that causes a target yield problem in a target yield problem stacking graph. For example, assuming that a weighted calculation result of a target yield problem stacking graph and a measurement data stacking graph corresponding to a WAT ​​A parameter is higher, it is considered that the target yield problem stacking graph is more matched with the WAT A parameter. At this time, the WAT A parameter can be considered to be the target measurement parameter of the target yield problem that causes the target yield problem stacking graph.

[0157] Specifically, the measurement data stacking graphs corresponding to each measurement parameter can be sorted according to the weighted calculation results, the measurement data stacking graph corresponding to the largest weighted calculation result can be determined as the maximum relevant measurement data stacking graph, and the measurement parameter corresponding to the maximum relevant measurement data stacking graph can be used as the target measurement parameter causing the target yield problem.

[0158] Preferably, in some cases, the matching data or the correlation data is relatively high, but the final weighted calculation result is relatively low. This does not mean that the target yield problem has nothing to do with the measurement parameter. In order to further ensure the accuracy of the obtained target measurement parameters, the matching data, the correlation data and the weighted calculation result can be combined to jointly decide the most relevant measurement parameters:

[0159] According to the matching degree data and the correlation data, the measurement data stacking graph with the highest matching degree and the measurement data stacking graph with the highest correlation with the target yield problem stacking graph can be determined respectively, and then based on the measurement data stacking graph with the highest matching degree and the measurement data stacking graph with the highest correlation, the highest matching measurement parameters and the highest correlation measurement parameters corresponding to the target yield problem stacking graph are determined; then, a rationality judgment process is performed on the highest matching measurement parameters, the highest correlation measurement parameters, the target measurement parameters and the target yield problem, so as to determine the measurement parameters most relevant to the target yield problem according to the rationality judgment results.

[0160] The measurement data stacking graph with the highest matching degree may refer to the measurement data stacking graph corresponding to the largest matching degree data after sorting the matching degree data, and the measurement parameter corresponding to the measurement data stacking graph with the highest matching degree is the measurement parameter with the highest matching degree. The measurement data stacking graph with the highest correlation may refer to the measurement data stacking graph corresponding to the largest correlation data after sorting the correlation data, and the measurement parameter corresponding to the measurement data stacking graph with the highest correlation is the measurement parameter with the highest correlation.

[0161] The rationality judgment process may refer to a pre-set process for judging the highest matching measurement parameters (measurement parameters corresponding to the measurement data stacking graph with the highest matching degree), the highest correlation measurement parameters (measurement parameters corresponding to the measurement data stacking graph with the highest correlation) and the target measurement parameters (measurement parameters of the most relevant measurement data stacking graph corresponding to the maximum weighted calculation result) when the numerical values ​​of the matching data, correlation data and weighted calculation results differ greatly.

[0162] For example, when it is detected that the difference between the matching data, the correlation data and the weighted calculation result is greater than a preset difference threshold, the determined highest matching measurement parameter, the highest correlation measurement parameter and the target measurement parameter can be sent to the management and control end, so that the relevant managers of the management and control end can further manually determine the measurement parameters that are most relevant to the target yield problem; the result can also be further determined by pre-set judgment conditions. For example, when the matching data or the correlation data is greater than 90%, and the weighted calculation result is less than 20%, the highest matching measurement parameter or the highest correlation measurement parameter corresponding to the matching data or the correlation data is used as the final analysis result. Of course, this is only an illustrative example, and the present exemplary embodiment does not make any special limitation on this.

[0163] In an example embodiment of the present disclosure, the matching degree data and the correlation data may be weighted by a pre-selected target weighted calculation method, wherein the target weighted calculation method may refer to a weighted calculation method determined in advance based on experimental data, which can correctly reflect the analysis results.

[0164] Specifically, before weighted calculation of the matching degree data and the correlation data, Figure 5 The steps in determine how the target weights are calculated:

[0165] refer to Figure 5 As shown, step S510, obtaining a sample yield problem stacking graph and a sample measurement data stacking graph pre-stored in a database; wherein the database also includes sample measurement parameters of the yield problem that causes the sample yield problem stacking graph;

[0166] Step S520, obtaining a plurality of pre-set weighted calculation methods;

[0167] Step S530, weighting and calculating the sample matching degree data and sample correlation data corresponding to the sample yield problem stacking graph and the sample measurement data stacking graph in sequence according to a plurality of weighted calculation methods;

[0168] Step S540 , determining a target weighted calculation method from the multiple weighted calculation methods according to the weighted calculation results corresponding to the multiple weighted calculation methods and the sample measurement parameters.

[0169] Among them, the sample target yield problem stacking graph and the sample measurement data stacking graph can refer to the sample data used for testing in the pre-established standard database. At this time, the sample target yield problem stacking graph and the sample measurement data stacking graph can be a 100% corresponding relationship. Then, the above method is used to perform weighted calculation on the matching data and correlation data corresponding to the sample target yield problem stacking graph and the sample measurement data stacking graph. If the weighted calculation result is 80%, it means that the corresponding weighted calculation method is poor, then other weighted calculation methods can be selected for recalculation until the weighted calculation result is infinitely close to 100%, and finally the weighted calculation method with the highest weighted calculation result is used as the target weighted calculation method.

[0170] The various weighted calculation methods may include a weighted calculation method for multiplying the matching data and the correlation data, or a weighted calculation method for respectively configuring weights for the matching data and the correlation data and adding them together. The specific weighted calculation method may be optimized and constructed by continuously changing different mathematical formulas during the experiment. Therefore, this example embodiment does not specifically limit the various weighted calculation methods.

[0171] By using the sample target yield problem stacking graph and the sample measurement data stacking graph in the pre-established standard database, a target weighted calculation method is selected from a variety of weighted calculation methods to further improve the accuracy of the weighted calculation results of the matching data and the correlation data corresponding to the target yield problem stacking graph and the measurement data stacking graph, improve the accuracy of the target measurement parameters, and determine the process flow that leads to the target yield problem based on the obtained target measurement parameters to improve the yield of the wafer.

[0172] Figure 6 The following schematic diagram shows an application of determining target measurement parameters according to an embodiment of the present disclosure.

[0173] refer to Figure 6 As shown, step S610, obtain the input wafer group and the target yield problem distribution graph corresponding to each wafer in the wafer group (such as LotA, waferB, LotC, LotD, waferE...), and obtain the input measurement parameters under different types of tests:

[0174] Step S620, based on the input measurement parameters under different types of tests, the target yield problem distribution graphs are stacked to obtain a target yield problem stacking graph 601 of the target yield problem distribution characteristics. For example, the target yield problem stacking graph 601 may include a failed wafer area 602 and a qualified wafer area other than the failed wafer area 602 in the target yield problem stacking graph 601. Of course, the target yield problem stacking graph 601 is only a schematic diagram and should not cause any special limitation to the present exemplary embodiment.

[0175] Step S630, feature matching is performed between the image features in the target yield problem stacking graph 601 and the measurement data stacking graphs under different types of tests (such as the stacking graph corresponding to the wafer group under MET A measurement parameters, the stacking graph corresponding to the wafer group under MET B measurement parameters, the stacking graph corresponding to the wafer group under WAT A measurement parameters, the stacking graph corresponding to the wafer group under WAT B measurement parameters, the stacking graph corresponding to the wafer group under Defect A measurement parameters, the stacking graph corresponding to the wafer group under Defect B measurement parameters, etc.) to obtain matching degree data, and a correlation curve is constructed to obtain correlation data;

[0176] Step S640, weighted calculation is performed on the obtained matching data and correlation data to obtain a weighted score (weighted calculation result), and finally the target measurement parameter is determined based on the weighted score, or the most relevant measurement parameter causing the target yield problem of the target yield problem stacking graph 601 is determined based on the rationality of the weighted score, the matching data and the correlation data corresponding to the target measurement parameter, the highest matching measurement parameter and the highest correlation measurement parameter, and finally the analysis result (and analysis process) can be visualized.

[0177] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0178] In addition, in this exemplary embodiment, a data analysis device is also provided. Figure 7 As shown, the data analysis device 700 includes: a graphic acquisition module 710, a correlation degree calculation module 720 and a target measurement parameter analysis module 730. Among them:

[0179] The pattern acquisition module 710 is used to acquire a target yield problem stacking pattern corresponding to a wafer group having a target yield problem, and to acquire a measurement data stacking pattern of the wafer group under different types of tests;

[0180] The correlation degree calculation module 720 is used to calculate the matching degree data and the correlation data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs, and perform weighted calculation on the matching degree data and the correlation data to obtain a weighted calculation result;

[0181] The target measurement parameter analysis module 730 is used to analyze the weighted calculation result to determine the target measurement parameter that causes the target yield problem.

[0182] In an exemplary embodiment of the present disclosure, based on the above solution, the association degree calculation module 720 further includes:

[0183] A matching degree data calculation unit, used for performing graphic matching between the target yield problem stacking graph and the measurement data stacking graph to obtain matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs;

[0184] The correlation data calculation unit is used to calculate the correlation data corresponding to each of the measurement data stacking patterns and the target yield problem stacking pattern.

[0185] In an exemplary embodiment of the present disclosure, based on the above solution, the correlation data calculation unit further includes:

[0186] A graphic partitioning subunit, used to partition the target yield problem stacked graphic and the measurement data stacked graphic to determine a main graphic area;

[0187] A wafer yield determination subunit, used to determine wafer yield data corresponding to the main graphic area in the target yield problem stacking graphic;

[0188] A statistical feature determination subunit, used to determine statistical feature data of the measurement parameters corresponding to the main graphic area in each of the measurement data stacked graphics;

[0189] The correlation data calculation subunit is used to calculate the correlation data between the measurement data stacking graph and each of the target yield problem stacking graphs through the wafer yield data and the statistical feature data in the main graph area.

[0190] In an exemplary embodiment of the present disclosure, based on the above solution, the graph acquisition module 710 further includes a target yield problem stacking graph acquisition unit, and the target yield problem stacking graph acquisition unit is configured as follows:

[0191] Acquire a wafer group having a target yield problem, each wafer in the wafer group having a target yield problem distribution pattern;

[0192] The target yield problem distribution graphs corresponding to the wafers are stacked to obtain a target yield problem stacking graph.

[0193] In an exemplary embodiment of the present disclosure, based on the above solution, the graphic acquisition module 710 further includes a measurement data stacking graphic acquisition unit, and the measurement data stacking graphic acquisition unit is configured as follows:

[0194] Acquire measurement data sets of each of the wafers in the wafer group under different types of tests;

[0195] Generating measurement data distribution graphs corresponding to different types of tests based on the measurement data set;

[0196] The measurement data distribution graphs are stacked to obtain a measurement data stacking graph corresponding to the wafer group.

[0197] In an exemplary embodiment of the present disclosure, based on the above solution, the matching degree data calculation unit further includes:

[0198] A graphic feature extraction subunit, used to extract a first graphic feature in the target yield problem stacked graphic, and to extract a second graphic feature in each of the measurement data stacked graphics;

[0199] a feature vector calculation subunit, configured to calculate a first feature vector corresponding to the first graphic feature and a second feature vector corresponding to the second graphic feature;

[0200] The matching degree calculation subunit is used to calculate the matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs according to the first feature vector and the second feature vector.

[0201] In an exemplary embodiment of the present disclosure, based on the above solution, the matching degree calculation subunit is further configured as follows:

[0202] Calculating similarity data between the first feature vector and the second feature vector;

[0203] The similarity data is used as matching data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs.

[0204] In an exemplary embodiment of the present disclosure, based on the above solution, the target measurement parameter analysis module 730 further includes a first analysis unit, and the first analysis unit is configured as follows:

[0205] sorting the measurement data stacked graphs according to the weighted calculation results, and determining the measurement data stacked graph corresponding to the largest weighted calculation result as the most relevant measurement data stacked graph;

[0206] The measurement parameter corresponding to the most relevant measurement data stacking pattern is used as the target measurement parameter causing the target yield problem.

[0207] In an exemplary embodiment of the present disclosure, based on the above solution, the target measurement parameter analysis module 730 further includes a second analysis unit, and the second analysis unit is configured as follows:

[0208] According to the matching degree data and the correlation data, respectively determining a measurement data stacking pattern having the highest matching degree and a measurement data stacking pattern having the highest correlation with the target yield problem stacking pattern;

[0209] Determine the highest matching measurement parameter and the highest correlation measurement parameter corresponding to the target yield problem stacking graph based on the measurement data stacking graph with the highest matching degree and the measurement data stacking graph with the highest correlation;

[0210] A rationality judgment process is performed on the highest matching measurement parameter, the highest correlation measurement parameter, the target measurement parameter and the target yield problem to determine the measurement parameter most relevant to the target yield problem according to the rationality judgment result.

[0211] In an exemplary embodiment of the present disclosure, based on the above solution, the data analysis device further includes a weighted calculation method screening unit, and the weighted calculation method screening unit is configured to:

[0212] Obtaining a sample yield problem stacking graph and a sample measurement data stacking graph pre-stored in a database; wherein the database also includes sample measurement parameters of the yield problem that causes the sample yield problem stacking graph;

[0213] Get multiple pre-set weighted calculation methods;

[0214] The sample yield problem stacking graph and the sample matching degree data and the sample correlation data corresponding to the sample measurement data stacking graph are weighted and calculated in sequence according to a plurality of weighted calculation methods;

[0215] A target weighted calculation method is determined from the multiple weighted calculation methods according to the weighted calculation results corresponding to the multiple weighted calculation methods and the sample measurement parameters.

[0216] In an exemplary embodiment of the present disclosure, based on the above-mentioned solution, the measurement parameters include one or more combinations of defect parameters, electrical parameters and structural parameters.

[0217] The specific details of each module of the above-mentioned data analysis device have been described in detail in the corresponding data analysis method, so they will not be repeated here.

[0218] It should be noted that although several modules or units of the data analysis device are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0219] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above data analysis method is also provided.

[0220] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0221] Refer to the following Figure 8 800 according to this embodiment of the present disclosure is described. Figure 8 The electronic device 800 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0222] like Figure 8 As shown, the electronic device 800 is in the form of a general computing device. The components of the electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one storage unit 820, a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810), and a display unit 840.

[0223] The storage unit stores a program code, which can be executed by the processing unit 810, so that the processing unit 810 performs the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 810 can perform the following steps: Figure 1In step S110 shown in the figure, a target yield problem stacking graph corresponding to a wafer group having a target yield problem is obtained, and a measurement data stacking graph of the wafer group under different types of tests is obtained; in step S120, the target yield problem stacking graph is matched with each of the measurement data stacking graphs to obtain matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs; in step S130, correlation data corresponding to each of the measurement data stacking graphs and the target yield problem stacking graph is calculated; in step S140, the matching degree data and the correlation data are weightedly calculated, and the target measurement parameters causing the target yield problem are determined according to the weighted calculation result.

[0224] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 821 and / or a cache memory unit 822 , and may further include a read-only memory unit (ROM) 823 .

[0225] The storage unit 820 may also include a program / utility 824 having a set (at least one) of program modules 825, such program modules 825 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0226] Bus 830 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0227] The electronic device 800 may also communicate with one or more external devices 870 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 800, and / or communicate with any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 850. Furthermore, the electronic device 800 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 860. As shown, the network adapter 860 communicates with other modules of the electronic device 800 via a bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0228] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0229] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0230] refer to Fig. 9 As shown, a program product 900 for implementing the above-mentioned data analysis method according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0231] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The 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 of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0232] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0233] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0234] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0235] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0236] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0237] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0238] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data analysis method, characterized in that: include: Obtaining a target yield problem stacking graph corresponding to a wafer group having a target yield problem, and obtaining a measurement data stacking graph of the wafer group under different types of tests; Performing graphic matching on the target yield problem stacking graph and each of the measurement data stacking graphs to obtain matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs; Calculating correlation data corresponding to each of the measurement data stacking graphs and the target yield problem stacking graph; A weighted calculation is performed on the matching degree data and the correlation data, and a target measurement parameter causing the target yield problem is determined according to the weighted calculation result.

2. The data analysis method according to claim 1, characterized in that: The step of obtaining a target yield problem stacking pattern corresponding to a wafer group having a target yield problem includes: Acquire a wafer group having a target yield problem, each wafer in the wafer group having a target yield problem distribution pattern; The target yield problem distribution graphs corresponding to the wafers are stacked to obtain a target yield problem stacking graph.

3. The data analysis method according to claim 1, characterized in that: The step of obtaining a stacked graph of measurement data of the wafer group under different types of tests includes: Acquire measurement data sets of each of the wafers in the wafer group under different types of tests; Generating measurement data distribution graphs corresponding to different types of tests based on the measurement data set; The measurement data distribution graphs are stacked to obtain a measurement data stacking graph corresponding to the wafer group.

4. The data analysis method according to claim 1, characterized in that: Performing graphic matching on the target yield problem stacking graph and each of the measurement data stacking graphs to generate matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs, including: Extracting a first graphic feature in the target yield problem stacked graph, and extracting a second graphic feature in each of the measurement data stacked graphs; Calculating a first eigenvector corresponding to the first graphic feature and a second eigenvector corresponding to the second graphic feature; Matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs is calculated according to the first feature vector and the second feature vector.

5. The data analysis method according to claim 4, characterized in that: The matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs is calculated according to the first feature vector and the second feature vector, including: Calculating similarity data between the first feature vector and the second feature vector; The similarity data is used as matching data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs.

6. The data analysis method according to claim 1, characterized in that: Calculating the correlation data corresponding to each of the measurement data stacking graphs and the target yield problem stacking graph, including: Partitioning the target yield problem stacked graph and the measurement data stacked graph to determine a main graph area; Correlation data between the target yield problem stacked pattern and each of the measurement data stacked patterns is calculated based on the main pattern area.

7. The data analysis method according to claim 6, characterized in that: Calculating correlation data between each of the measurement data stacking patterns and the target yield problem stacking pattern based on the main pattern area includes: Determine wafer yield data corresponding to the main graphic area in the target yield problem stacking graphic; Determine statistical characteristic data of the measurement parameters corresponding to the main graphic area in each of the measurement data stacked graphics; The correlation data between the target yield problem stacking graph and each of the measurement data stacking graphs is calculated using the wafer yield data and the statistical feature data in the main graph area.

8. The data analysis method according to claim 1, characterized in that: Determining the target measurement parameter causing the target yield problem according to the weighted calculation result includes: sorting the measurement data stacked graphs according to the weighted calculation results, and taking the measurement data stacked graph corresponding to the largest weighted calculation result as the most relevant measurement data stacked graph; The measurement parameter corresponding to the most relevant measurement data stacking pattern is used as the target measurement parameter causing the target yield problem.

9. The data analysis method according to claim 8, characterized in that: After obtaining the most relevant measurement data stacking graph and taking the measurement parameter corresponding to the most relevant measurement data stacking graph as the target measurement parameter, the method further includes: According to the matching degree data and the correlation data, respectively determining a measurement data stacking pattern having the highest matching degree and a measurement data stacking pattern having the highest correlation with the target yield problem stacking pattern; Determine the highest matching measurement parameter and the highest correlation measurement parameter corresponding to the target yield problem stacking graph based on the measurement data stacking graph with the highest matching degree and the measurement data stacking graph with the highest correlation; A rationality judgment process is performed on the highest matching measurement parameter, the highest correlation measurement parameter, the target measurement parameter and the target yield problem to determine the measurement parameter most relevant to the target yield problem according to the rationality judgment result.

10. The data analysis method according to claim 1, characterized in that: The weighted calculation of the matching degree data and the correlation data includes: The matching degree data and the correlation data are weightedly calculated by a pre-selected target weighted calculation method.

11. The data analysis method according to claim 10, characterized in that: Before performing weighted calculation on the matching degree data and the correlation data, the method further includes: Obtaining a sample yield problem stacking graph and a sample measurement data stacking graph pre-stored in a database; wherein the database also includes sample measurement parameters of the yield problem that causes the sample yield problem stacking graph; Get multiple pre-set weighted calculation methods; The sample yield problem stacking graph and the sample matching degree data and the sample correlation data corresponding to the sample measurement data stacking graph are weighted and calculated in sequence according to a plurality of weighted calculation methods; A target weighted calculation method is determined from the multiple weighted calculation methods according to the weighted calculation results corresponding to the multiple weighted calculation methods and the sample measurement parameters.

12. The data analysis method according to claim 1, characterized in that: The measurement parameters include one or a combination of defect parameters, electrical parameters and structural parameters.

13. A data analysis device, characterized in that: include: A pattern acquisition module, used to acquire a target yield problem stacking pattern corresponding to a wafer group having a target yield problem, and to acquire a measurement data stacking pattern of the wafer group under different types of tests; A correlation degree calculation module, used to calculate the matching degree data and the correlation data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs, and perform weighted calculation on the matching degree data and the correlation data to obtain a weighted calculation result; The target measurement parameter analysis module is used to analyze the weighted calculation result to determine the target measurement parameter that causes the target yield problem.

14. The data analysis device according to claim 13, characterized in that: The association degree calculation module also includes: A matching degree data calculation unit, used for performing graphic matching between the target yield problem stacking graph and the measurement data stacking graph to obtain matching degree data corresponding to the target yield problem stacking graph and each of the measurement data stacking graphs; The correlation data calculation unit is used to calculate the correlation data corresponding to each of the measurement data stacking patterns and the target yield problem stacking pattern.

15. The data analysis device according to claim 14, characterized in that: The correlation data calculation unit also includes: A graphic partitioning subunit, used to partition the target yield problem stacked graphic and the measurement data stacked graphic to determine a main graphic area; A wafer yield determination subunit, used to determine wafer yield data corresponding to the main graphic area in the target yield problem stacking graphic; A statistical feature determination subunit, used to determine statistical feature data of the measurement parameters corresponding to the main graphic area in each of the measurement data stacked graphics; The correlation data calculation subunit is used to calculate the correlation data between the target yield problem stacking graph and each of the measurement data stacking graphs through the wafer yield data and the statistical feature data in the main graph area.

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