WO2023184281A1

WO2023184281A1 - Inspection parameter analysis method and apparatus

Info

Publication number: WO2023184281A1
Application number: PCT/CN2022/084212
Authority: WO
Inventors: 王瑜; 吴建波; 代言玉; 吴建民; 柴栋; 王洪; 李园园; 王萍; 王建宙; 沈国梁; 陈韵
Original assignee: 京东方科技集团股份有限公司; 北京中祥英科技有限公司
Priority date: 2022-03-30
Filing date: 2022-03-30
Publication date: 2023-10-05
Also published as: WO2023184281A9; CN117157541A

Abstract

An inspection parameter analysis method and apparatus. The method comprises: acquiring a plurality of groups of first inspection parameters of a product, wherein the first inspection parameters comprise inspection parameters of a plurality of measurement points, and the measurement points are position points on the product; according to an interpolation algorithm, performing interpolation processing on the plurality of groups of first inspection parameters to determine a plurality of groups of second inspection parameters, wherein the number of second inspection parameters is the same as the number of first inspection parameters; according to a correlation analysis algorithm, determining an evaluation value of correlation between the plurality of groups of second inspection parameters, wherein the evaluation value of correlation is used for representing the correlation between the plurality of groups of second inspection parameters corresponding to each of the measurement points; and outputting the evaluation value of correlation between the plurality of groups of second inspection parameters.

Description

A detection parameter analysis method and device

Technical field

The present application relates to the field of data analysis, and in particular to a detection parameter analysis method and device.

Background technique

In the semiconductor and panel industries, due to the influence of various production processes or equipment, there will be problems with defective points on the products produced. At present, after some key coating layers of Glass are completed, the key parameters of the coating layer will be detected through manual inspection.

However, due to the complexity of the inspection process and the huge amount of data, relying solely on manual inspection to locate the cause of defects has extremely limited processing time and accuracy, making it difficult to meet the growing production needs.

Contents of the invention

On the one hand, a detection parameter analysis method is provided, which method includes: obtaining multiple sets of first detection parameters of a product; the first detection parameters include detection parameters of multiple measurement points, and the measurement points are on the product. position point; according to the interpolation algorithm, perform interpolation processing on multiple sets of first detection parameters to determine multiple sets of second detection parameters; the number of groups of the second detection parameters is the same as the number of groups of the first detection parameters; according to the correlation Analysis algorithm, determines the correlation evaluation value between multiple sets of second detection parameters; the correlation evaluation value is used to characterize the correlation between the multiple sets of second detection parameters corresponding to each of the measurement points; output Correlation evaluation values between multiple sets of second detection parameters.

In some embodiments, the above-mentioned interpolation algorithm is the Kriging interpolation method; according to the interpolation algorithm, interpolation processing is performed on multiple sets of first detection parameters to determine multiple sets of second detection parameters, including: determining the relationship between the first measurement point and the second measurement point. The coordinate distance and semivariance between Semivariance determines the semivariance of multiple prediction points; the prediction point is a measurement point that does not have a corresponding detection parameter in each group of first detection parameters among multiple measurement points corresponding to multiple sets of first detection parameters; according to Determine the weight coefficient based on the semivariance of multiple prediction points; determine the target interpolation values of multiple prediction points based on the weight coefficient and multiple sets of first detection parameters; perform interpolation on multiple sets of first detection parameters based on the target interpolation values of multiple prediction points Processing and determining multiple sets of second detection parameters.

In some embodiments, determining the semi-variance of multiple prediction points based on the coordinate distance and the semi-variance between the first measurement point and the second measurement point includes: based on the coordinate distance between the first measurement point and the second measurement point. The coordinate distance and semivariance are used to determine the semivariance fitting curve; based on the semivariance fitting curve, the semivariance of multiple prediction points is determined.

In some embodiments, the coordinate distance between the above-mentioned first measurement point and the second measurement point satisfies the following formula:

Among them, d _ij represents the coordinate distance between the first measurement point and the second measurement point, i represents the number of the first measurement point, _xi represents the abscissa of the first measurement point, and y _i represents the ordinate of the first measurement point. , j represents the number of the second measurement point, x _j represents the abscissa of the second measurement point, and y _j represents the ordinate of the second measurement point.

The semivariance between the first measurement point and the second measurement point satisfies the following formula:

Among them, r _ij represents the semivariance between the first measurement point and the second measurement point, E represents the covariance, z _i represents the detection parameter of the first measurement point, and z _j represents the detection parameter of the second measurement point.

The target interpolation of multiple prediction points satisfies the following formula:

in,

Represents the target interpolation of multiple prediction points, λ _k represents the weight coefficient, and z _k represents the detection parameter of a measurement point numbered k.

In some embodiments, the above correlation analysis algorithm is Pearson correlation analysis method, and the correlation evaluation values between multiple sets of second detection parameters satisfy the following formula:

Among them, ρ represents the correlation evaluation value, X and Y respectively represent a second detection parameter, μ _X represents the average value of the second detection parameter X, μ _Y represents the average value of the second detection parameter _Y , and σ The standard deviation of the detection parameter X, σ _Y represents the standard deviation of the second detection parameter Y.

In some embodiments, the above-mentioned correlation analysis algorithm is the Kruskal-Wallis test method; according to the correlation analysis algorithm, determining the correlation evaluation values between multiple sets of second detection parameters includes: according to Sort the multiple sets of second detection parameters in increasing order; determine the ranks of the multiple sets of second detection parameters; determine the statistics of the multiple sets of second detection parameters based on the ranks of the multiple sets of second detection parameters; based on the multiple sets of second detection parameters The statistic of the second detection parameter determines the correlation evaluation values between multiple sets of second detection parameters.

In some embodiments, the statistics of the plurality of sets of second detection parameters satisfy the following formula:

Among them, H represents the statistics of multiple sets of second detection parameters, N represents the number of detection parameters included in multiple sets of second detection parameters, n represents the number of detection parameters included in one second detection parameter, and _R The sum of the ranks of the detection parameters X, R _Y represents the sum of the ranks of the second detection parameter Y.

The correlation evaluation values between multiple sets of second detection parameters satisfy the following formula:

Among them, P represents the correlation evaluation value between multiple sets of second detection parameters, H represents the statistics of multiple sets of second detection parameters, k represents the number of second detection parameters, and Γ represents the gamma distribution function.

In some embodiments, the method further includes: determining contour plots of multiple sets of second detection parameters; the contour plots are used to characterize the magnitude of detection parameters corresponding to each area on the product; and outputting the multiple sets of second detection parameters. Contour map.

In some embodiments, obtaining the first detection parameters of the product includes: determining a statistical aggregation table; and obtaining multiple sets of first detection parameters of the product according to the statistical aggregation table.

In some embodiments, the statistical aggregation table is the Haidup database HBase statistical aggregation table. The above determination of the HBase statistical aggregation table includes: obtaining the third detection parameters of multiple products from the detection device according to the Haidup database; the third detection The parameters include the first detection parameter; data aggregation of the third detection parameters of multiple products is performed according to the structured query language SQL to determine the HBase statistical aggregation table.

Obtain the third detection parameters of multiple products from the detection equipment; the third detection parameters include the first detection parameters; determine the HBase statistical aggregation table according to the third detection parameters of the multiple products; obtain the first detection parameter of the product according to the HBase statistical aggregation table. Detect parameters.

In some embodiments, the above-mentioned multiple sets of first detection parameters include key process parameters and electro-permanent magnet EPM electrical parameters; the key process parameters include surface resistance RS parameters, alignment accuracy TP parameters, line width CD parameters, and film thickness THK parameters. , and at least one of the fitting accuracy OL parameters; the electrical parameters include at least one of the threshold voltage VTH parameter, the mobility MOB parameter, the operating current ION parameter, and the reverse cut-off current IOFF parameter.

In some embodiments, the method further includes: before outputting the correlation evaluation values between the plurality of sets of second detection parameters, sorting the correlation evaluation values between the plurality of sets of second detection parameters.

On the other hand, a parameter analysis device is provided, including: an acquisition unit, a processing unit and an output unit; the acquisition unit is configured to acquire multiple sets of first detection parameters of the product; the first detection parameters include detection parameters of multiple measurement points , the measurement point is a position point on the product; the processing unit is configured to perform interpolation processing on multiple sets of first detection parameters according to the interpolation algorithm to obtain multiple sets of second detection parameters; the number of second detection parameters is the same as the first detection parameter The number is the same; the processing unit is also configured to determine the correlation evaluation values between multiple sets of second detection parameters according to the correlation analysis algorithm; the correlation evaluation values are used to characterize the multiple sets of second detection corresponding to each measurement point Correlation between parameters; the output unit is configured to output correlation evaluation values of multiple sets of second detection parameters.

In some embodiments, the processing unit is further configured to determine the coordinate distance and semivariance between the first measurement point and the second measurement point; the first measurement point and the second measurement point are measurement points among the plurality of measurement points. ; The processing unit is further configured to determine the semivariance of the plurality of prediction points according to the distance between the first measurement point and the second measurement point and the semivariance; the processing unit is further configured to determine the semivariance of the plurality of prediction points according to the distance between the first measurement point and the second measurement point and the semivariance; , determine the weight coefficient; the processing unit is also configured to determine the target interpolation of multiple prediction points based on the weight coefficient and multiple sets of first detection parameters; the processing unit is also configured to determine the target interpolation of multiple prediction points based on the target interpolation of the multiple prediction points. A set of first detection parameters is interpolated to determine multiple sets of second detection parameters.

In some embodiments, the processing unit is further configured to determine the semivariance fitting curve according to the coordinate distance between the first measurement point and the second measurement point and the semivariance; the processing unit is further configured to determine the semivariance fitting curve according to the semivariance fitting curve. The combined curve is used to determine the semivariance of multiple prediction points.

In some embodiments, the coordinate distance between the first measurement point and the second measurement point satisfies the following formula:

in,

In some embodiments, the correlation analysis algorithm is Pearson correlation analysis method, and the correlation evaluation values between multiple sets of second detection parameters satisfy the following formula:

In some embodiments, the processing unit is further configured to sort the plurality of sets of second detection parameters in increasing order; the processing unit is further configured to determine the ranks of the sorted plurality of sets of second detection parameters; the processing unit, It is also configured to determine the statistics of multiple sets of second detection parameters based on the ranks of the multiple sets of second detection parameters; the processing unit is also configured to determine the multiple sets of second detection parameters based on the statistics of the multiple sets of second detection parameters. correlation evaluation value.

In some embodiments, the processing unit is also configured to determine contour plots of multiple sets of second detection parameters; the contour plots are used to characterize the magnitude of the detection parameters corresponding to each area on the product; the output unit is also configured Contour plots of multiple sets of second detection parameters are output.

In some embodiments, the processing unit is further configured to determine a statistical aggregation table; the obtaining unit is further configured to obtain multiple sets of first detection parameters of the product according to the statistical aggregation table.

In some embodiments, the acquisition unit is further configured to acquire third detection parameters of multiple products from the detection device according to the Haidup database; the third detection parameters include the first detection parameters; and the processing unit is further configured to acquire the third detection parameters of the plurality of products according to the Haidup database. The structured query language SQL aggregates data on the third detection parameters of multiple products and determines the HBase statistical aggregation table.

In some embodiments, the multiple sets of first detection parameters include key process parameters and electropermanent magnet EPM electrical parameters; the key process parameters include surface resistance RS parameters, alignment accuracy TP parameters, line width CD parameters, film thickness THK parameters, and at least one of the fitting accuracy OL parameters; the electrical parameters include at least one of the threshold voltage VTH parameter, the mobility MOB parameter, the operating current ION parameter, and the reverse cut-off current IOFF parameter.

In some embodiments, the processing unit is further configured to sort the correlation evaluation values between the plurality of sets of second detection parameters before outputting the correlation evaluation values between the plurality of sets of second detection parameters.

On the other hand, a detection parameter analysis application is provided, wherein the detection parameter analysis application includes an application interactive interface. When a preset operation is performed on the application interactive interface, the detection parameter analysis application is executed as described in any of the above embodiments. Parameter analysis device method.

In yet another aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores computer program instructions. When the computer program instructions are run on a computer (for example, a parameter analysis device), they cause the computer to execute the parameter analysis device method as described in any of the above embodiments.

In yet another aspect, a computer program product is provided. The computer program product includes computer program instructions. When the computer program instructions are executed on a computer (for example, a parameter analysis device), the computer program instructions cause the computer to execute the parameter analysis device method as described in any of the above embodiments.

In yet another aspect, a computer program is provided. When the computer program is executed on a computer (for example, a parameter analysis device), the computer program causes the computer to execute the parameter analysis device method as described in any of the above embodiments.

Description of drawings

In order to explain the technical solutions in the present disclosure more clearly, the drawings required to be used in some embodiments of the present disclosure will be briefly introduced below. Obviously, the drawings in the following description are only appendices of some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings. In addition, the drawings in the following description can be regarded as schematic diagrams and are not intended to limit the actual size of the product, the actual flow of the method, the actual timing of the signals, etc. involved in the embodiments of the present disclosure.

Figure 1 is a schematic diagram of an application scenario of a parameter analysis method according to some embodiments;

Figure 2 is a flow chart of a parameter analysis method provided according to some embodiments;

Figure 3 is an application interaction interface provided according to some embodiments;

Figure 4 is a schematic line diagram of a correlation evaluation value provided according to some embodiments;

Figure 5 is a flow chart of another parameter analysis method provided according to some embodiments;

Figure 6 is a flow chart of another parameter analysis method provided according to some embodiments;

Figure 7 is a flow chart of another parameter analysis method provided according to some embodiments;

Figure 8 is a contour map provided according to some embodiments;

Figure 9 is another contour map provided in accordance with some embodiments;

Figure 10 is a structural diagram of a parameter analysis device provided according to some embodiments;

Figure 11 is a structural diagram of another parameter analysis device provided according to some embodiments.

Detailed ways

The technical solutions in some embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments provided by this disclosure, all other embodiments obtained by those of ordinary skill in the art fall within the scope of protection of this disclosure.

Unless the context otherwise requires, throughout the specification and claims, the term "comprise" and its other forms such as the third person singular "comprises" and the present participle "comprising" are used. Interpreted as open and inclusive, it means "including, but not limited to." In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "example", "specific "example" or "some examples" and the like are intended to indicate that a particular feature, structure, material or characteristic associated with the embodiment or example is included in at least one embodiment or example of the present disclosure. The schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be included in any suitable manner in any one or more embodiments or examples.

Hereinafter, the terms “first” and “second” are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the embodiments of the present disclosure, unless otherwise specified, "plurality" means two or more.

"At least one of A, B and C" has the same meaning as "at least one of A, B or C" and includes the following combinations of A, B and C: A only, B only, C only, A and B The combination of A and C, the combination of B and C, and the combination of A, B and C.

"A and/or B" includes the following three combinations: A only, B only, and a combination of A and B.

As used herein, the term "if" is optionally interpreted to mean "when" or "in response to" or "in response to determining" or "in response to detecting," depending on the context. Similarly, depending on the context, the phrase "if it is determined..." or "if [stated condition or event] is detected" is optionally interpreted to mean "when it is determined..." or "in response to the determination..." or “on detection of [stated condition or event]” or “in response to detection of [stated condition or event]”.

The use of "suitable for" or "configured to" in this document implies open and inclusive language that does not exclude devices that are suitable for or configured to perform additional tasks or steps.

Additionally, the use of "based on" is meant to be open and inclusive in that a process, step, calculation or other action "based on" one or more stated conditions or values may in practice be based on additional conditions or beyond the stated values.

As used herein, "about," "approximately," or "approximately" includes the stated value as well as an average within an acceptable range of deviations from the particular value, as determined by one of ordinary skill in the art. Determined taking into account the measurement in question and the errors associated with the measurement of the specific quantity (i.e., the limitations of the measurement system).

As used herein, "parallel," "perpendicular," and "equal" include the stated situation as well as situations that are approximate to the stated situation within an acceptable deviation range, where Such acceptable deviation ranges are as determined by one of ordinary skill in the art taking into account the measurement in question and the errors associated with the measurement of the particular quantity (ie, the limitations of the measurement system). For example, "parallel" includes absolutely parallel and approximately parallel, and the acceptable deviation range of approximately parallel may be, for example, a deviation within 5°; "perpendicular" includes absolutely vertical and approximately vertical, and the acceptable deviation range of approximately vertical may also be, for example, Deviation within 5°. "Equal" includes absolute equality and approximate equality, wherein the difference between the two that may be equal within the acceptable deviation range of approximately equal is less than or equal to 5% of either one, for example.

It will be understood that when a layer or element is referred to as being on another layer or substrate, this can mean that the layer or element is directly on the other layer or substrate, or that the layer or element can be coupled to the other layer or substrate There is an intermediate layer in between.

In the following, nouns involved in the embodiments of the present disclosure are explained to facilitate readers' understanding.

(1)Hadoop database (HBase) statistical aggregation table

HBase is a distributed storage system for storing structured data. HBase is a distributed massive list non-relational database, that is, the data in HBase is stored based on column families, and a column family contains several columns. HBase can be used when real-time reading and writing and random access to very large data sets are required.

The HBase statistical aggregation table is a table for statistical data based on the data set stored in HBase and using mature computer languages such as structured query language (SQL) to perform aggregation operations.

Correspondingly, based on the update of the data source, the HBase statistical aggregation table can also be updated synchronously. Exemplarily, in some embodiments provided by the present disclosure, the HBase statistical aggregation table stores various detection parameters about the product obtained from the production equipment, such as key process parameters and electro permanent magnet (EPM) electrical parameters. Key process parameters may include: resistance surface (RS) parameters, total pitch (TP) parameters, critical dimension (CD) parameters, film thickness (thickness, THK) parameters, and fitting accuracy. OL parameters. EPM electrical parameters may include: threshold voltage (voltage of threshold, VTH) parameters, mobility (MOB) parameters, operating current (represented by ION) parameters, and reverse cutoff current (represented by IOFF) parameters. Moreover, every certain preset time period (the preset time period can be set manually), the HBase statistical aggregation table will update its own stored detection parameters based on the update of the detection parameters of the production equipment.

(2) Interpolation algorithm

The interpolation algorithm is an important method for discrete function approximation. It can be used to estimate the approximate value of the function at other points through the value of the function at a limited number of points.

In the field of mathematics, interpolation refers to the interpolation of a continuous function based on discrete data so that this continuous curve passes through all given discrete data points. Interpolation is an important method for approximating discrete functions. It can be used to estimate the approximate value of a function at other points through the value of the function at a limited number of points. In the field of images, interpolation is used to fill the gaps between pixels when the image is transformed.

For example, Kriging interpolation is a commonly used interpolation algorithm in the field of data analysis.

Illustratively, in some embodiments provided by the present disclosure, the parameter analysis device can interpolate multiple different types of detection parameters obtained from measurement points through the Kriging interpolation method to ensure the unity of these multi-category detection parameters, This is to avoid negative impacts on the correlation analysis of detection parameters due to differences in the distribution and number of measurement points.

(3) Contour map

Contour maps are generally used in the fields of geographical exploration and map drawing. They connect points with the same height on the surface to form a loop and directly project it onto the plane to form a horizontal curve. Loops with different heights will not coincide.

Generally in the field of geographical technology, contour maps are widely used. To put it simply, a contour map connects points with the same height on the surface into a loop and directly projects it onto the plane to form a horizontal curve. Loops at different heights will not coincide unless the surface shows cliffs or cliffs, which will cause the lines to be too dense and overlap. For example, if there is a flat and open hillside on the surface, the distance between the curves will be quite wide, and its baseline is based on the mean tide line of the sea level. There are marking instructions at the bottom of each map for the convenience of users. , the main illustrations include scale, drawing number, frame connection table, legend and azimuth angle.

In some embodiments provided by the present disclosure, the parameter analysis device is based on detection parameters detected at measurement points in different areas of the product. After interpolating these detection parameters, a contour map is drawn based on these and detection parameters. It is used to intuitively represent the size of the detection parameters in different areas of the product to assist staff in analyzing the correlation analysis results of the detection parameters.

(4)Correlation analysis algorithm

Correlation analysis algorithm refers to an algorithm that analyzes two or more correlated variable elements to measure the close correlation between two variable factors.

Correlation analysis refers to the analysis of two or more correlated variable elements to measure the close correlation between the two variable factors. There needs to be a certain connection or probability between the correlation elements before correlation analysis can be performed. Correlation is not equal to causation, nor is it simple personalization. The scope and fields covered by correlation cover almost every aspect we see. The definition of correlation in different disciplines is also very different.

There needs to be a certain connection or probability between the correlation elements before correlation analysis can be performed.

For example, the Pearson algorithm, the Kruskal-Wallist (K-W) test method and the Mann-Whitney (M-W) rank sum test method are the fields of data correlation analysis. The most commonly used algorithm.

Illustratively, in some embodiments provided by the present disclosure, the parameter analysis device will perform correlation analysis on multiple categories of detection parameters of the interpolated product based on the Pearson algorithm and the K-W test method, so that the staff can perform correlation analysis based on Correlation analysis results are used to identify defective areas of the product and make process improvements or equipment troubleshooting.

The nouns involved in the embodiments of the present disclosure have been described above.

Currently in the semiconductor and panel industries, in the field of thin film transistor liquid crystal display (TFT-LCD), defects in TFT-LCD products may occur during production. The occurrence of defects may be caused by any manufacturing process or equipment in the entire production line. Product defects can usually be reflected based on some key parameters of the product.

Therefore, at this stage, after the manufacturing of some key film layers of the product is completed, the key parameters of the product will be tested. The staff analyzes these key parameters through manual inspection to determine whether there are defects in the product and the causes of the defects. Then the staff will improve the manufacturing process or troubleshoot the manufacturing equipment based on the causes of the defects. However, due to the complex manufacturing process and large quantity of products, relying on manual inspection to locate the cause of defects is extremely limited in timeliness and accuracy, making it difficult to meet the growing production demand.

In the existing technology, two solutions are provided for analyzing the causes of product defects:

Solution 1: A defect pattern analysis method based on bad maps (CN112184691A). This solution is aimed at a certain product type and organizes the defect measurement results of the same product from different sources and the various characteristic measurement values of the product into certain standards according to certain standards. Displays the coordinate data information associated with the panel Map coordinates. Taking the defective coordinate position information of the display panel as the analysis object, a density clustering model of defective data information and display panel data is established for different product types. The clustering categories depend on the defect information of the corresponding display panel production tools and product characteristics and defects. The correlation degree of coordinates is used to determine the similarity between the bad information of each product and the corresponding density clustering type through the similarity coefficient of the density clustering model, and several effective bad types are screened out. In summary, this solution quickly locates bad information into bad types.

Option 2: A bad root cause path analysis method and system based on association rule mining (CN111932394A). Through the bad root cause path analysis method and system based on association rule mining, a large number of non-suspicious site devices can be quickly and automatically filtered. Narrowing down the scope of analysis eliminates the need for manual intervention by inputting additional experience and knowledge; and the present invention traverses all possible site equipment combinations, sorts them in descending order of improvement, and automatically highlights the most suspicious combinations among a large number of possible path combinations, which can assist staff in rapid positioning The path to the site device that caused the bad root cause to occur. In summary, this solution is based on an improved association rule mining algorithm, traverses all possible device path combinations, and automatically and quickly locates the root cause of the problem.

Due to the abnormal correlation between the detection parameters (such as electrical parameters and key process parameters) of liquid crystal display (LCD) and organic light-emitting diode (OLED) products, it will directly lead to product defects, and Depending on the degree of abnormality in the correlation between detection parameters, the types of defects in products are also different. Therefore, it is extremely necessary to quantify the correlation between detection parameters into indicators, so that the root cause of the defects in the previous site can be quickly located, so as to Business personnel adjust detection parameters efficiently and timely for verification testing and maintenance. However, neither of the above two solutions involves the correlation between detection parameters, and cannot quickly locate the root cause of the pre-sequence site.

In view of this, the present disclosure provides a parameter analysis method and device to solve the problem in the prior art that when analyzing product detection parameters, the processing time limit and accuracy are limited, making it difficult to meet the growing production demand. The method provided by this disclosure can also quantify the correlation between parameters into indicators and quickly locate the root cause of the failure of the previous site, so that business personnel can adjust parameters in an efficient and timely manner for verification, testing and maintenance.

It should be pointed out that in the parameter analysis method provided by the present disclosure, the execution subject is the parameter analysis device. The parameter analysis device may be a server, or may be a part of a device coupled to the server, such as a chip system in the server. The parameter analysis device includes:

Processor, the processor can be a general central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more programs for controlling the disclosed solution implemented integrated circuit.

Transceiver, a transceiver can be any device such as a transceiver used to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks, WLAN) etc.

Memory, memory can be read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (random access memory, RAM) or other types that can store information and instructions. Type of dynamic storage device, it can also be electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disk storage, optical disc Storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or can be used to carry or store the desired program code in the form of instructions or data structures and can be used by Any other media accessible by a computer, but not limited to this. The memory can exist independently and be connected to the processor through communication lines. Memory can also be integrated with the processor.

It should be noted that the various embodiments of the present disclosure can be used for reference or reference to each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can be referred to each other without limitation.

The implementation of the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

As shown in Figure 1, Figure 1 is a schematic diagram of an application scenario of a parameter analysis method provided according to some embodiments. In the application scenario of Figure 1, a parameter analysis device 10 and a production equipment 20 are included.

Among them, the parameter analysis device 10 is used to obtain detection parameters from the production equipment 20 and perform parameter analysis so that business personnel can conduct verification, testing and maintenance of the production equipment 20 based on the results of parameter analysis.

Production equipment 20, used for producing products. And in the production equipment 20, various types of inspection stations are provided. A testing station is used to measure a type of testing parameters of a product. Correspondingly, after the production equipment 20 obtains the detection parameters of the product according to the detection station set by itself, it sends these detection parameter items to the parameter analysis device 10 .

For example, in practical applications, for a production line in a factory used to produce products, the parameter analysis device 10 summarizes the detection parameters collected by the detection stations set by the production equipment 20 set on the production line, and performs parameter analysis and analyzes the parameters. After the results are output, the staff can remotely determine the cause of the defective product through the analysis results, and then improve the production process or repair the production equipment 20 on the production line.

As shown in Figure 2, Figure 2 is a parameter analysis method provided according to some embodiments. The method includes the following steps:

Step 201: The parameter analysis device obtains multiple sets of first detection parameters of the product.

Among them, the product can be a panel, flat module or display product. When the product is a display product, the display product may include at least one of a glass screen (Class), a liquid crystal screen (Liquid Crystal Display, LCD), and a plasma display panel (Plasma Display Panel, PDP).

Among them, the product testing parameters can be divided into key process parameters and EPM electrical parameters. The key process parameters include at least one of the following parameters: surface resistance RS parameter, alignment accuracy TP parameter, line width CD parameter, film thickness THK parameter, and alignment accuracy OL parameter. The EPM electrical parameters include at least one of the following parameters: threshold voltage VTH parameter, mobility MOB parameter, operating current ION parameter, and reverse cut-off current IOFF parameter. Correspondingly, the first detection parameter includes at least one of the key process parameters and at least one of the EPM electrical parameters, that is, a group of first detection parameters includes the same key process parameter or EPM electrical parameter.

For example, assuming that the product is a glass screen (Class), after the Class is produced through production equipment, its corresponding key process parameters can include: the surface resistance RS parameter of the Class, the alignment accuracy TP parameter, the line width CD parameter, and the film thickness. The corresponding EPM electrical parameters of the THK parameter and fitting accuracy OL parameter may include: the threshold voltage VTH parameter, the mobility MOB parameter, the operating current ION parameter, and the reverse cut-off current IOFF parameter of the Class.

For example, the parameter analysis device can obtain the reverse cut-off current IOFF parameters and film thickness THK parameters of different measurement points on the Class, and use the obtained reverse cut-off current IOFF parameters and film thickness THK parameters as the first detection parameters of the Class; Alternatively, the parameter analysis device can obtain the reverse cut-off current IOFF parameters and surface resistance RS parameters of different measurement points on the Class, and use the obtained reverse cut-off current IOFF parameters and surface resistance RS parameters as the first detection parameters of the Class.

It should be understood that the measurement point is the location point selected on the product for measuring the detection parameters by the parameter analysis device when acquiring the detection parameters of the product. Generally, for a piece of product, the number of measurement points is between 20 and 180.

In a possible implementation, the parameter analysis device obtains multiple sets of first detection parameters of the product from a statistical aggregation table. Among them, as mentioned above, the statistical aggregation table includes: RS parameters, TP parameters, CD parameters, THK parameters, OL parameters, VTH parameters, MOB parameters, ION parameters, and IOFF parameters of the product. Optionally, the parameter analysis device obtains third detection parameters of multiple products from the detection equipment. The third detection parameters of the products include all types of key process parameters and EPM electrical parameters of the products.

Illustratively, the above statistical aggregation table is an HBase statistical aggregation table. When the statistical aggregation table is an HBase statistical aggregation table, the parameter analysis device can obtain the third detection parameters of multiple products from the detection device according to HBase. Among them, the detection equipment is the equipment installed on the production equipment of the product for obtaining the detection parameters of the product.

Further, the parameter analysis device performs data aggregation on the third detection parameters of multiple products based on SQL, stores them in table form, and determines the HBase statistical aggregation table.

Wherein, the third detection parameter includes the first detection parameter. Furthermore, the HBase statistical aggregation table determined based on the third detection parameter includes the first detection parameter, and the parameter analysis device obtains the first detection parameter of the product from the HBase statistical aggregation table according to the user's detection requirements.

Among them, the staff's detection requirements can be used to instruct the user to detect the first detection parameters required for detection. The detection requirements include the identification information of the first detection parameters to be detected and other information, such as the filtering conditions of the first detection parameters, etc., in accordance with Only the first detection parameters of these screening conditions can be used for detection analysis. Optionally, the staff can send the detection requirement to the parameter analysis device through the application interactive interface, so that the parameter analysis device obtains the first detection parameter of the product from the HBase statistical aggregation table according to the detection requirement.

Illustratively, as shown in Figure 3, the parameter analysis device provides an application interactive interface, and the staff can set the acquisition conditions of the first detection parameter on this application interaction interface. After the acquisition conditions of the first detection parameter are set, Afterwards, the staff performs a confirmation operation to obtain multiple sets of first detection parameters from the data stored in the HBase statistical aggregation table. For example, the staff can set to obtain the products with factory number ARRAY, model number BNA650QU5V402, Class ID 1, and

detection site numbers

990G and 576K between July 1, 2021 and July 22, 2021. The first detection parameter whose detection parameter type is IOFF1_20 (that is, IOFF parameter) and THICKNESS (that is, THK parameter).

Optionally, after acquiring multiple sets of first detection parameters of Glass from the HBase statistical aggregation table according to the conditions set by the staff, the parameter analysis device stores the multiple sets of first detection parameters in the form of a table, as shown in Table 1 below. Shown:

Table 1 Data table of IOFF parameters and THK parameters of selected Class ID1

Among them, Step represents the site number of the detection station, Item represents the type of detection parameter, x and y represent the abscissa and ordinate of the measurement point respectively, and Value represents the specific value of the detection parameter.

Step 202: The parameter analysis device performs interpolation processing on multiple sets of first detection parameters according to the interpolation algorithm, and determines multiple sets of second detection parameters.

Among them, the interpolation algorithm is an interpolation algorithm. For example, the interpolation algorithm may be Kriging interpolation method or other interpolation algorithms, which is not specifically limited in this disclosure. The specific implementation process of interpolating multiple sets of first detection parameters according to the Kriging interpolation method can be referred to as shown in Figure 5 below, and will not be described again here.

It should be noted that the number of sets of second detection parameters is the same as the number of sets of first detection parameters. That is to say, the number of categories of detection parameters will not change before and after the interpolation process. The second detection parameter is only obtained by interpolating the corresponding first detection parameter according to the interpolation algorithm by the parameter analysis device. Compared with the first detection parameters, some new parameter values of prediction points are added to the second detection parameters.

Wherein, the prediction point is a measurement point that does not have a corresponding detection parameter in each set of first detection parameters among all measurement points corresponding to multiple sets of first detection parameters. For example, taking the first detection parameter as having two groups, namely the IOFF parameter and the THK parameter, the parameter analysis device divides the Class into 16*14 grid points. At this time, there are a total of 224 position points corresponding to the interpolation process. That is, the goal of interpolation processing is to make both the IOFF parameter and the THK parameter have parameter values of 224 identical position points.

At this time, for the IOFF parameters, the selection of prediction points can be divided into two types:

Case 1: For some measurement points, the THK parameters include the detection parameters of these measurement points, and the IOFF parameters do not include them, then these measurement points are used as prediction points when interpolating the IOFF parameters.

Case 2: For some measurement points, neither the IOFF parameters nor the THK parameters include the detection parameters of these measurement points, then these measurement points are used as prediction points when interpolating the IOFF parameters.

Similarly, for THK parameters, the selection of prediction points can also be divided into two types:

Case 3: For some measurement points, the IOFF parameters include the detection parameters of these measurement points, and the THK parameters do not include them, then these measurement points are used as prediction points when interpolating the THK parameters.

Case 4: For some measurement points, neither the THK parameters nor the IOFF parameters include the detection parameters of these measurement points, then these measurement points are used as prediction points when interpolating the THK parameters.

It should be understood that for a set of first detection parameters, the prediction points are only the position points selected by the interpolation algorithm for this set of first detection parameters and to be interpolated. The parameter values of these prediction points are calculated according to the interpolation algorithm, and the parameter analysis device does not actually measure the first detection parameters of the group at the prediction points.

In this way, the parameter value of the newly added prediction point is combined with the parameter value included in the first detection parameter and obtained after actual detection of the measurement point to form the second detection parameter.

Illustratively, based on the example in step 201, the parameter analysis device divides Class into 16*14 grid points, one grid point serves as a measurement point or prediction point, and one measurement point or prediction point corresponds to one or more groups. Detect parameters. It is assumed that the parameter analysis device obtains two sets of first detection parameters of Class, which are IOFF parameters and THK parameters respectively. Among them, there are 100 measurement points for the IOFF parameter, that is, the IOFF parameter includes a total of IOFF values of 100 position points. There are 140 measurement points for THK parameters, that is, the THK parameters include a total of 140 THK values of position points. Among the measurement points corresponding to the IOFF parameter and the THK parameter, some are the same and some are different. At this time, it is necessary to interpolate the IOFF parameters and THK parameters according to the interpolation algorithm to ensure the unity of the IOFF and THK parameters and facilitate subsequent correlation analysis.

Further, after the parameter analysis device performs interpolation processing on the two sets of first detection parameters: IOFF parameters and THK parameters, the IOFF parameters include IOFF values of 224 position points, of which the IOFF values of 100 position points are actual detection measurements. The IOFF value of the point, and the IOFF value of the other 124 position points are the newly added IOFF values after interpolation processing according to the interpolation algorithm. The same is true for the THK parameters. Among the THK parameters after interpolation processing, the THK values of 140 position points are the IOFF values of the actual detected measurement points, and the THK values of the other 84 position points are new after interpolation processing based on the interpolation algorithm. Increased THK value. After that, the parameter analysis device uses the interpolated IOFF parameters and THK parameters, each including parameter values of 224 position points, as multiple sets of second detection parameters. It can be understood that at this time, for all position points included in the two sets of first detection parameters (that is, the 224 position points mentioned above), each position point has a corresponding IOFF parameter value and a THK parameter value, so the The uniformity of the two first detection parameters after interpolation is improved.

Step 203: The parameter analysis device determines correlation evaluation values between multiple sets of second detection parameters according to the correlation analysis algorithm.

Among them, the correlation analysis algorithm is a correlation analysis algorithm. The correlation evaluation values between multiple sets of second detection parameters can be used to characterize the degree of correlation between multiple sets of second detection parameters. For example, the correlation analysis algorithm may be at least one of the Pearson algorithm and the K-W test method. The specific implementation process of Pearson algorithm can be based on the following formula (4)-formula (5). The specific implementation process of the K-W test method can be referred to as shown in Figure 6 below, and will not be described again here.

It should be noted that the parameter analysis device can simultaneously determine the correlation evaluation values between multiple sets of second detection parameters based on multiple correlation analysis algorithms. Correspondingly, each correlation analysis algorithm has its corresponding correlation evaluation value. The staff can study the process problems reflected in the detection parameters of Class based on the correlation evaluation values obtained by multiple algorithms. Compared with Instead of evaluating Class's process problems based only on the correlation evaluation value obtained by one algorithm, using multiple correlation evaluation algorithms to evaluate Class's process problems can better ensure the accuracy and efficiency of process improvement or equipment maintenance.

Illustratively, based on the example in step 202, the second detection parameters include the IOFF parameter and the THK parameter, and both the IOFF parameter and the THK parameter undergo interpolation processing. The parameter analysis device performs correlation analysis on the IOFF parameters and THK parameters according to the correlation analysis algorithm, and obtains the correlation evaluation results of the IOFF parameters and THK parameters. For the IOFF parameter value and THK parameter value of each measurement point, there is a The correlation evaluation value represents the degree of correlation between the IOFF parameter and the THK parameter above this measurement point.

Step 204: The parameter analysis device outputs correlation evaluation values between multiple sets of second detection parameters.

Optionally, the parameter analysis device can output correlation evaluation values between multiple sets of second detection parameters in various ways. The correlation evaluation value is used to characterize the correlation between multiple sets of second detection parameters corresponding to each measurement point.

In a possible implementation, as shown in FIG. 4 , the parameter analysis device outputs the correlation evaluation values between multiple sets of second detection parameters in the form of a line graph.

Optionally, before outputting the correlation evaluation values between the multiple sets of second detection parameters, the parameter analysis device sorts the correlation evaluation values between the multiple sets of second detection parameters, so that the staff can intuitively see The degree of correlation between the detection parameters corresponding to each measurement point.

It should be noted that after obtaining the correlation evaluation values between the multiple sets of second detection parameters output by the parameter analysis device, the staff can determine the defective areas of the product based on the correlation evaluation values.

For example, the staff compares the correlation evaluation value of the detection parameter of each measurement point on the product reflected in the correlation evaluation value with the preset threshold to determine whether the process evaluation result of the measurement point is good or bad. For example, when the correlation analysis algorithm is the Pearson algorithm and the preset threshold is set to 0.65, assume that the correlation evaluation value of the IOFF parameter and the THK parameter of a certain measurement point is 0.7. Since 0.7 is greater than 0.65, the process evaluation value of this measurement point The result is good.

After that, the staff determines the set of measurement points with poor process evaluation results as the poor process areas of the product.

Based on the above technical solution, the parameter analysis device in the present disclosure obtains multiple types of detection parameters of the product, and performs interpolation processing on each type of detection parameters through an interpolation algorithm to determine the multiple types of interpolated detection parameters, After interpolation processing, the data unity between each detection parameter can be maintained to provide support for subsequent correlation analysis; after that, the parameter analysis device correlates various types of detection parameters after interpolation according to the correlation analysis algorithm. evaluation to determine the specific correlation evaluation value. This quantifies the correlation between the detection parameters, and the staff can quickly locate the bad areas reflected by the detection parameters at the detection site, so that the staff can adjust the parameters efficiently and timely for verification testing and maintenance, effectively improving In order to ensure processing timeliness and accuracy, it can meet the growing production needs.

As a possible embodiment of the present disclosure, as shown in Figure 5 in conjunction with Figure 2, when the interpolation algorithm is the Kriging interpolation method, the above step 202 specifically includes the following steps:

Step 501: The parameter analysis device determines the coordinate distance and semivariance between the first measurement point and the second measurement point.

It should be noted that the first detection parameters include detection parameters for multiple measurement points. For example, taking the product as Class, the first detection parameters include the IOFF parameter and the THK parameter. At this time, the IOFF parameter includes the IOFF values of multiple measurement points on Class, and the THK parameter includes the THK values of multiple measurement points on Class.

The first measurement point and the second measurement point are measurement points among multiple measurement points corresponding to the same group of first detection parameters. For example, the first detection parameter includes an IOFF parameter and a THK parameter, then the first measurement point and the second measurement point are any two measurement points corresponding to the IOFF parameter, or the first measurement point and the second measurement point are corresponding to the THK parameter. any two measurement points.

In a possible implementation, the parameter analysis device first determines the coordinates of the first measurement point and the second measurement point, and then calculates the distance between the first measurement point and the second measurement point. Exemplarily, calculating the distance between the first measurement point and the second measurement point satisfies the following formula 1:

In a possible implementation, the parameter analysis device first calculates the covariance between the first measurement point and the second measurement point, and calculates the semivariance between the first measurement point and the second measurement point based on this. Exemplarily, calculating the semivariance between the first measurement point and the second measurement point satisfies the following formula 2:

Step 502: The parameter analysis device determines semi-variances of multiple prediction points based on the distance and semi-variance between the first measurement point and the second measurement point.

The parameter analysis device determines the points within a specific neighborhood range of the measurement points corresponding to the multiple sets of first detection parameters, or a specific number of adjacent points, as multiple prediction points.

Illustratively, corresponding to the example in step 202, there are currently two first detection parameters, which are the IOFF parameter and the THK parameter. Among them, there are 100 measurement points for the IOFF parameter, that is, the IOFF parameter includes a total of 100 points of IOFF values, and there are 140 measurement points for the THK parameter, that is, the THK parameter includes a total of 140 points of THK values. Now, the IOFF parameter needs to be interpolated so that the IOFF parameter includes the parameter values of 224 points. Similarly, the THK parameter value is the same. Then the 124 points corresponding to the new parameter value in the IOFF parameter and the 84 points corresponding to the new parameter value in the THK parameter are the prediction points.

In a possible implementation, the parameter analysis device determines the semivariance fitting curve based on the coordinate distance and the semivariance between the first measurement point and the second measurement point. After that, the parameter analysis device determines the semivariance of multiple prediction points according to the semivariance fitting curve.

It should be noted that the above-mentioned semi-variance fitting curve is calculated by the parameter analysis device after the distance and semi-variance between all possible any two points among the measurement points corresponding to the multiple sets of first detection parameters are completed. The distances and semivariances corresponding to all measurement points are plotted into a scatter plot, and an optimal curve is found for fitting.

Further, the parameter analysis device can obtain the functional expression r=r(d) of the semivariance fitting curve. It can be understood that the specific expression form of the function expression r=r(d) is determined by the fitting curve and will vary according to the values of multiple sets of first detection functions, and will not be described in detail in this embodiment. Therefore, the parameter analysis device can determine the semivariance corresponding to each prediction point based on the coordinates of the prediction point.

Step 503: The parameter analysis device determines the weight coefficient based on the semivariance of multiple prediction points.

The weight coefficient is used to weight and sum the parameter values of all measurement points included in the multiple sets of first detection parameters to determine the target interpolation of the prediction point. The weight coefficient λ _k here satisfies the estimated value

The optimal set of coefficients with the smallest difference from the true value z ₀ , that is

At the same time, the conditions for unbiased estimation are satisfied

That is, the covariance between the target interpolation value of the prediction point calculated by the parameter analysis device and the true value of the prediction point is 0.

It should be noted that in the Kriging interpolation method, the weight coefficient is determined by the semivariance function r=r(d) of the semivariance fitting curve in step 502. For a specific method of determining the weight coefficient λ _k based on the semivariance function r=r(d), reference may be made to the Kriging interpolation method, which will not be described in detail in the embodiments of this disclosure.

Step 504: The parameter analysis device determines the target interpolation values of multiple prediction points based on the weight coefficients and multiple sets of first detection parameters.

Optionally, after calculating the weight coefficient, the parameter analysis device multiplies and sums the detection parameters of each measurement point included in the plurality of sets of first detection parameters by its corresponding weight coefficient to obtain a plurality of prediction points. target interpolation.

In a possible implementation, the target interpolation of multiple prediction points satisfies the following formula 3:

in,

represents the target interpolation of the plurality of prediction points, λ _k represents the weight coefficient, and z _k represents the detection parameter of the measurement point numbered k.

Step 505: The parameter analysis device performs interpolation processing on multiple sets of first detection parameters based on the target interpolation of multiple prediction points, and determines multiple sets of second detection parameters.

Illustratively, corresponding to the example in the aforementioned step 202, the product is Class, and the parameter analysis device divides Class into 16*14 grid points. One grid point serves as a measurement point or prediction point, and the IOFF parameter and the THK parameter are Perform interpolation processing. That is, after interpolation processing, the IOFF parameters include IOFF values of 224 points. Among them, the IOFF values of 100 points are the IOFF values of the actual detected measurement points, and the IOFF values of the other 124 points are parameters. The analysis device performs interpolation processing on the IOFF parameter based on the target interpolation of multiple prediction points and adds a new IOFF value. The same is true for the THK parameters. Among the THK parameters after interpolation processing, the THK values of 140 points are the IOFF values of the actual measured measurement points, and the THK values of the other 84 points are the parameter analysis device based on multiple prediction points. The target interpolation is the newly added THK value after interpolating the THK parameters.

After that, the parameter analysis device uses the interpolated IOFF parameters and THK parameters, each including parameter values of 224 points, as multiple sets of second detection parameters.

Based on the above technical solution, the parameter analysis device in the present disclosure performs interpolation processing on multiple types of detection parameters through the Kriging interpolation method. The interpolation processing in this step can maintain the data uniformity between each detection parameter, so as to facilitate the subsequent steps. Evaluate the correlation between product detection parameters to improve the accuracy of correlation evaluation.

As a possible embodiment of the present disclosure, the correlation analysis algorithm may be Pearson correlation analysis method.

The Pearson correlation coefficient is a method of measuring the similarity of data. It is used to describe the tendency of two sets of data to change and move together. It is a value between -1 and 1. When the linear relationship between two sets of data increases, the correlation coefficient tends to -1 or 1; when one variable increases, the other variable also increases, indicating that there is a positive correlation between them, and the correlation coefficient is greater than 0; when one variable increases When the other variable is large, it indicates that there is a negative correlation between them, and the correlation coefficient is less than 0; if the correlation coefficient is equal to 0, it indicates that there is no linear correlation between them.

For example, the calculation formula of the Pearson correlation analysis method is expressed as the covariance of two variables divided by the standard deviation of the two variables. Combined with the example of step 202, in the embodiment of the present disclosure, the correlation evaluation values between multiple sets of second detection parameters satisfy the following formula 4:

In the field of mathematics, the calculation method of standard deviation is common knowledge, so the above formula 4 can be transformed into formula 5:

Combined with the _example of step 202, X can be expressed as n detection values ₍ X ₁ , detection values (Y ₁ , Y ₂ ,…, Y _n ).

The correlation analysis algorithm is introduced above when it is the Pearson correlation analysis method. Through this algorithm parameter analysis device, the correlation evaluation values between multiple sets of second detection parameters can be determined, so that the staff can determine based on the correlation analysis results. Identify defective areas of the product and make process improvements or equipment troubleshooting.

As a possible embodiment of the present disclosure, as shown in Figure 6 in conjunction with Figure 2, when the correlation analysis algorithm is the K-W test method, step 203 specifically includes the following steps:

Step 601: The parameter analysis device sorts multiple sets of second detection parameters in increasing order.

Illustratively, based on the example in step 202, there are currently two first detection parameters, namely the IOFF parameter and the THK parameter. Sample X is used to represent the IOFF parameter, and sample Y is used to represent the THK parameter. Then both sample X and sample Y include n parameter values, in this example, n=16*14=224. After that, the parameter analysis device arranges all N (N=2*n) parameter values in a row in increasing order.

Step 602: The parameter analysis device determines the ranks of the multiple sets of sorted second detection parameters.

Optionally, after determining the ranks of the multiple sets of sorted second detection parameters, the parameter analysis device sums the ranks of each second detection parameter.

Illustratively, based on the example in step 601, Rx represents the sum of the ranks of the IOFF parameters in the sorting, and Ry represents the sum of the ranks of the THK parameters in the sorting.

Step 603: The parameter analysis device determines the statistics of the multiple sets of second detection parameters based on the ranks of the multiple sets of second detection parameters.

Illustratively, combined with the example in step 602, the statistics satisfy the following formula 6:

Step 604: The parameter analysis device determines the correlation evaluation values between the multiple sets of second detection parameters based on the statistics of the multiple sets of second detection parameters.

Illustratively, based on the example in step 603, the correlation evaluation values between multiple sets of second detection parameters satisfy the following formula 7:

Optionally, generally when the value of P is less than 0.05, the second detection parameter X and the second detection parameter Y are considered to be relatively relevant.

The correlation analysis algorithm is introduced above when it is the K-W test method. Through this algorithm parameter analysis device, the correlation evaluation value between multiple sets of second detection parameters can be determined, so that the staff can determine the product based on the correlation analysis results. defective areas and make process improvements or equipment troubleshooting.

As a possible embodiment of the present disclosure, the correlation analysis algorithm may be the M-W rank sum test method.

The main idea of the M-W rank sum test method is to assume that the two parameter samples participating in the analysis come from two identical populations except for the overall mean. The purpose is to test whether there is a significant difference in the means of the two populations. The specific algorithm steps of the M-W rank sum test method are as follows:

Step 1: Mix the two sets of data and arrange the levels in order of size. The smallest data level is 1, the second smallest data level is 2, and so on (if there are equal data, the average of these data sortings is taken as its level).

Step 2: Find the grades and W ₁ and W ₂ of the two samples respectively.

Step 3: Calculate the MW rank sum test statistics U ₁ and U ₂ of the two parameter samples.

Illustratively, U ₁ satisfies the following formula 8:

Illustratively, _U2 satisfies the following equation 9:

Among them, n ₁ is the size of the first sample, and n ₂ is the size of the second sample.

Further, select the smallest one between U ₁ and U ₂ and compare it with the critical value U _a . When U < U _a , it is considered that the two parameter samples are more relevant; when U > U _a , it is considered that the two parameter samples are more related. irrelevant. For example, the value of U _a is generally selected as 0.05.

The correlation analysis algorithm is introduced above when it is the M-W rank sum test method. Through this algorithm parameter analysis device, the correlation evaluation value between multiple sets of second detection parameters can be determined, so that the staff can determine based on the correlation analysis results. Identify defective areas of the product and make process improvements or equipment troubleshooting.

It should be noted that the correlation analysis algorithm in the embodiment of the present disclosure can also include multiple correlation analysis algorithms. For example, the correlation analysis algorithm can include Pearson correlation analysis method, K-W test method and M-W rank sum test method at the same time, or it can also include Pearson correlation analysis method, K-W test method and M-W rank sum test method at the same time. Includes other correlation analysis algorithms. It can be understood that the correlation analysis algorithm includes multiple correlation analysis algorithms, and multiple correlation analysis results can be obtained, thereby providing more evidence for the staff.

For example, when the correlation analysis algorithm includes both Pearson correlation analysis method and K-W test method, combined with the example in step 201 above, in step 204, the parameter analysis device may output the correlation analysis results in the form of Table 2 below.

Table 2 Correlation analysis results table

Among them, Step represents the site number of the detection station, and Item represents the type of detection parameters.

As a possible embodiment of the present disclosure, as shown in Figure 7 in conjunction with Figure 2, the parameter analysis method provided by the present disclosure also includes the following steps:

Step 701: The parameter analysis device determines contour maps of multiple sets of second detection parameters.

Among them, the contour map is used to characterize the size of the detection parameters corresponding to each area on the product. At this stage, contour maps are generally used in the fields of geographical exploration and map drawing. They connect points with the same height on the surface to form a loop and directly project it onto the plane to form a horizontal curve. Loops with different heights will not coincide. This characteristic of the contour map, when combined with the embodiments of the present disclosure, can intuitively represent the size of the detection parameters in different areas of the product, so as to assist the staff in analyzing the correlation analysis results of the detection parameters.

Step 702: The parameter analysis device outputs multiple sets of contour maps of the second detection parameters.

Optionally, the parameter analysis device can output multiple sets of contour maps of the second detection parameters in various ways.

In one possible implementation, combined with the example in step 202, Figure 8 and Figure 9 show two contour maps. Figure 8 represents the contour map of the IOFF parameters in the Glass, and Figure 9 represents the Contour plot of THK parameters in Glass. It is not difficult to see that the closer to the middle area of Glass, the smaller the IOFF parameter value is, and conversely, the larger the THK parameter is. Therefore, the staff can clearly see that there is a negative correlation between the IOFF parameter and the THK parameter.

Embodiments of the present disclosure can divide the parameter analysis device into functional modules or functional units according to the above method examples. For example, each functional module or functional unit can be divided corresponding to each function, or two or more functions can be integrated into one in the processing module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules or functional units. Among them, the division of modules or units in the embodiments of the present disclosure is schematic and is only a logical function division. In actual implementation, there may be other division methods.

As shown in Figure 10, it is a schematic structural diagram of a parameter analysis device 1000 provided according to some embodiments. The device includes: an acquisition unit 1001, a processing unit 1002 and an output unit 1003.

Among them, the obtaining unit 1001 is configured to obtain the first detection parameter of the product. For example, with reference to Figure 2, the acquisition unit 1001 is specifically configured to perform step 201.

The processing unit 1002 is configured to perform interpolation processing on the first detection parameter according to the interpolation algorithm to obtain the second detection parameter. For example, with reference to Figure 2, the processing unit 1002 is specifically configured to perform step 202.

The processing unit 1002 is further configured to determine the correlation evaluation value between the second detection parameters according to the correlation analysis algorithm. For example, with reference to Figure 2, the processing unit 1002 is specifically configured to perform step 203.

The output unit 1003 is configured to output the correlation evaluation value of the second detection parameter. For example, with reference to Figure 2, the output unit 1003 is specifically used to perform step 204.

In some embodiments, the processing unit 1002 is further configured to determine the coordinate distance and the semivariance between the first measurement point and the second measurement point. For example, with reference to Figure 5, the processing unit 1002 is specifically configured to perform step 501.

In some embodiments, the processing unit 1002 is further configured to determine semi-variances of the plurality of prediction points based on the distance between the first measurement point and the second measurement point and the semi-variance. For example, with reference to Figure 5, the processing unit 1002 is specifically configured to perform step 502.

In some embodiments, the processing unit 1002 is further configured to determine the weight coefficient according to the semi-variance of multiple prediction points. For example, with reference to Figure 5, the processing unit 1002 is specifically configured to perform step 503.

In some embodiments, the processing unit 1002 is further configured to determine target interpolations of multiple prediction points based on weight coefficients and multiple sets of first detection parameters. For example, with reference to Figure 5, the processing unit 1002 is specifically configured to perform step 504.

In some embodiments, the processing unit 1002 is further configured to perform interpolation processing on multiple sets of first detection parameters according to target interpolation of multiple prediction points, and determine multiple sets of second detection parameters. For example, with reference to Figure 5, the processing unit 1002 is specifically configured to perform step 505.

In some embodiments, the processing unit 1002 is further configured to determine a semivariance fitting curve according to the coordinate distance and the semivariance between the first measurement point and the second measurement point. For example, with reference to Figure 5, the processing unit 1002 is specifically configured to perform step 502.

In some embodiments, the processing unit 1002 is further configured to determine semivariances of multiple prediction points according to the semivariance fitting curve. For example, with reference to Figure 5, the processing unit 1002 is specifically configured to perform step 502.

In some embodiments, the processing unit 1002 is further configured to sort the plurality of sets of second detection parameters in increasing order. For example, with reference to Figure 6, the processing unit 1002 is specifically configured to perform step 601.

In some embodiments, the processing unit 1002 is further configured to determine the ranks of the multiple sets of sorted second detection parameters. For example, with reference to Figure 6, the processing unit 1002 is specifically configured to perform step 602.

In some embodiments, the processing unit 1002 is further configured to determine statistics of multiple sets of second detection parameters based on ranks of multiple sets of second detection parameters. For example, with reference to Figure 6, the processing unit 1002 is specifically configured to perform step 603.

In some embodiments, the processing unit 1002 is further configured to determine correlation evaluation values between multiple sets of second detection parameters based on statistics of multiple sets of second detection parameters. For example, with reference to Figure 6, the processing unit 1002 is specifically configured to perform step 604.

In some embodiments, the processing unit 1002 is further configured to determine contour maps of multiple sets of second detection parameters. For example, with reference to Figure 7, the processing unit 1002 is specifically configured to perform step 701.

In some embodiments, the output unit 1003 is further configured to output contour plots of multiple sets of second detection parameters. For example, with reference to Figure 6, the output unit 1003 is specifically used to perform step 702.

In some embodiments, the processing unit 1002 is further configured to determine a statistical aggregation table. For example, with reference to Figure 2, the processing unit 1002 is specifically configured to perform step 201.

In some embodiments, the acquisition unit 1001 is further configured to acquire multiple sets of first detection parameters of the product according to the statistical aggregation table. For example, with reference to Figure 2, the acquisition unit 1001 is specifically configured to perform step 201.

In some embodiments, the acquisition unit 1001 is further configured to acquire third detection parameters of multiple products from the detection device according to the Haidup database. For example, with reference to Figure 2, the acquisition unit 1001 is specifically configured to perform step 201.

In some embodiments, the processing unit 1002 is further configured to perform data aggregation on the third detection parameters of the multiple products according to the structured query language SQL, and determine the HBase statistical aggregation table. For example, with reference to Figure 2, the processing unit 1002 is specifically configured to perform step 201.

In some embodiments, the processing unit 1002 is further configured to sort the correlation evaluation values between the multiple sets of second detection parameters before outputting the correlation evaluation values between the multiple sets of second detection parameters. For example, with reference to Figure 2, the processing unit 1002 is specifically configured to perform step 204.

Optionally, the parameter analysis device 1000 may also include a storage unit (shown as a dotted box in FIG. 10 ), which stores programs or instructions. When the processing unit 1002 executes the program or instruction, the parameter analysis device 1000 can execute the parameter analysis method described in the above method embodiment.

In addition, the technical effects of the parameter analysis device described in FIG. 10 can be referred to the technical effects of the parameter analysis method described in the above embodiments, which will not be described again here.

Figure 11 shows another possible structural schematic diagram of the parameter analysis device involved in the above embodiment. The parameter analysis device 1100 includes: a processor 1102 and a communication interface 1103. The processor 1102 is configured to control and manage the actions of the parameter analysis device 1100, for example, perform the steps performed by the above-mentioned acquisition unit 1001, the processing unit 1002 and the output unit 1003, and/or be configured to perform other techniques described herein. process. The communication interface 1103 is configured to support communication between the parameter analysis device 1100 and other network entities. The parameter analysis device 1100 may further include a memory 1101 and a bus 1104 , the memory 1101 being configured to store program codes and data of the parameter analysis device 1100 .

The memory 1101 may be the memory in the parameter analysis device 1100, etc. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, Hard disk or solid state drive; the memory may also include a combination of the above types of memory.

The above-described processor 1102 may implement or execute various exemplary logical blocks, modules and circuits described in connection with the present disclosure. The processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various illustrative logical blocks, modules and circuits described in connection with this disclosure. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

The bus 1104 may be an Extended Industry Standard Architecture (EISA) bus or the like. The bus 1104 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 Figure 11, but it does not mean that there is only one bus or one type of bus.

The parameter analysis device 1100 in Figure 11 can also be a chip. The chip includes one or more (including two) processors 1102 and communication interfaces 1103.

Optionally, the chip also includes a memory 1101, which may include read-only memory and random access memory, and provides operating instructions and data to the processor 1102. Part of the memory 1101 may also include non-volatile random access memory (NVRAM).

In some embodiments, memory 1101 stores elements, execution modules, or data structures, or subsets thereof, or extended sets thereof.

In the embodiment of the present disclosure, the corresponding operation is performed by calling the operation instructions stored in the memory 1101 (the operation instructions may be stored in the operating system).

Through the above description of the embodiments, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated as needed. It is completed by different functional modules, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. For the specific working processes of the systems, devices and units described above, reference can be made to the corresponding processes in the foregoing method embodiments, which will not be described again here.

Some embodiments of the present disclosure provide a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) having computer program instructions stored therein, and the computer program instructions are stored in a computer (e.g., parameters When running on the analysis device), the computer is caused to execute the parameter analysis method as described in any of the above embodiments.

Illustratively, the computer-readable storage medium may include, but is not limited to: magnetic storage devices (such as hard disks, floppy disks or magnetic tapes, etc.), optical disks (such as CD (Compact Disk, compressed disk), DVD (Digital Versatile Disk, etc.) Digital versatile disk), etc.), smart cards and flash memory devices (e.g., EPROM (Erasable Programmable Read-Only Memory, Erasable Programmable Read-Only Memory), cards, sticks or key drives, etc.). The various computer-readable storage media described in this disclosure may represent one or more devices and/or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and/or carrying instructions and/or data.

Some embodiments of the present disclosure also provide a computer program product, for example, the computer program product is stored on a non-transitory computer-readable storage medium. The computer program product includes computer program instructions. When the computer program instructions are executed on a computer (for example, a parameter analysis device), the computer program instructions cause the computer to perform the parameter analysis method as described in the above embodiment.

Some embodiments of the present disclosure also provide a computer program. When the computer program is executed on a computer (for example, a parameter analysis device), the computer program causes the computer to perform the parameter analysis method as described in the above embodiment.

The beneficial effects of the above computer-readable storage media, computer program products and computer programs are the same as the beneficial effects of the parameter analysis methods described in some of the above embodiments, and will not be described again here.

In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.

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

In addition, each functional unit in various embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that come to mind within the technical scope disclosed by the present disclosure by any person familiar with the technical field should be covered. within the scope of this disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

A detection parameter analysis method, including:

Obtain multiple sets of first detection parameters of the product; the first detection parameters include detection parameters of multiple measurement points, and the measurement points are position points on the product;

According to the interpolation algorithm, interpolation processing is performed on the plurality of groups of first detection parameters to determine multiple groups of second detection parameters; the number of groups of the second detection parameters is the same as the number of groups of the first detection parameters;

According to the correlation analysis algorithm, the correlation evaluation value between the multiple sets of second detection parameters is determined; the correlation evaluation value is used to characterize the relationship between the multiple sets of second detection parameters corresponding to each of the measurement points. relevance;

Correlation evaluation values between the plurality of sets of second detection parameters are output.
The method according to claim 1, wherein the interpolation algorithm is Kriging interpolation method;

Performing interpolation processing on the multiple sets of first detection parameters according to the interpolation algorithm to determine multiple sets of second detection parameters includes:

Determine the coordinate distance and semivariance between the first measurement point and the second measurement point; the first measurement point and the second measurement point are among the plurality of measurement points corresponding to the same group of the first detection parameters. measuring points;

According to the distance between the first measurement point and the second measurement point and the semivariance, the semivariances of multiple prediction points are determined; the prediction points are the plurality of prediction points corresponding to the plurality of sets of first detection parameters. Among the measurement points, there are no measurement points that have corresponding detection parameters in each group of the first detection parameters;

Determine the weight coefficient according to the semi-variance of the multiple prediction points;

Determine target interpolations of the plurality of prediction points according to the weight coefficient and the plurality of sets of first detection parameters;

According to the target interpolation of the plurality of prediction points, the plurality of sets of first detection parameters are interpolated to determine the plurality of sets of second detection parameters.
The method according to claim 2, wherein determining the semi-variance of a plurality of prediction points based on the coordinate distance and the semi-variance between the first measurement point and the second measurement point includes:

Determine a semivariance fitting curve according to the coordinate distance and semivariance between the first measurement point and the second measurement point;

According to the semivariance fitting curve, semivariances of the plurality of prediction points are determined.
The method according to claim 2 or 3, wherein the coordinate distance between the first measurement point and the second measurement point satisfies the following formula:

Where, d ij represents the coordinate distance between the first measurement point and the second measurement point, i represents the number of the first measurement point, xi represents the abscissa of the first measurement point, y i represents the ordinate of the first measurement point, j represents the number of the second measurement point, x j represents the abscissa of the second measurement point, y j represents the ordinate of the second measurement point;

The semivariance between the first measurement point and the second measurement point satisfies the following formula:

Where, r ij represents the semivariance between the first measurement point and the second measurement point, E represents the covariance, z i represents the detection parameter of the first measurement point, and z j represents the second measurement Point detection parameters;

The target interpolation of the multiple prediction points satisfies the following formula:

in,
represents the target interpolation of the plurality of prediction points, λ k represents the weight coefficient, and z k represents the detection parameter of the measurement point numbered k.
The method according to claim 1, wherein the correlation analysis algorithm is Pearson correlation analysis method, and the correlation evaluation values between the plurality of sets of second detection parameters satisfy the following formula:

Wherein, ρ represents the correlation evaluation value, X and Y respectively represent one of the second detection parameters, μ X represents the average value of the second detection parameter X, μ Y represents the average value of the second detection parameter Y, and σ X represents the standard deviation of the second detection parameter X, and σ Y represents the standard deviation of the second detection parameter Y.
The method according to claim 1, wherein the correlation analysis algorithm is the Kruskal-Wallis test method;

Determining the correlation evaluation values between the plurality of sets of second detection parameters according to the correlation analysis algorithm includes:

Sorting the plurality of sets of second detection parameters in increasing order;

Determine the ranks of the sorted plurality of sets of second detection parameters;

Determine statistics of the plurality of sets of second detection parameters according to the ranks of the plurality of sets of second detection parameters;

Correlation evaluation values between the plurality of sets of second detection parameters are determined according to the statistics of the plurality of sets of second detection parameters.
The method according to claim 6, wherein the statistics of the plurality of sets of second detection parameters satisfy the following formula:

Wherein, H represents the statistics of the multiple sets of second detection parameters, N represents the number of detection parameters included in the multiple sets of second detection parameters, and n represents the number of detection parameters included in one of the second detection parameters. , R X represents the sum of the ranks of the second detection parameter X, R Y represents the sum of the ranks of the second detection parameter Y;

The correlation evaluation values between the multiple sets of second detection parameters satisfy the following formula:

Wherein, P represents the correlation evaluation value between multiple sets of second detection parameters, H represents the statistics of the multiple sets of second detection parameters, k represents the number of the second detection parameters, and Γ represents the gamma distribution function.
The method according to any one of claims 1-7, further comprising:

Determine the contour plots of the plurality of sets of second detection parameters; the contour plots are used to characterize the size of the detection parameters corresponding to each area on the product;

Contour plots of the plurality of sets of second detection parameters are output.
The method according to any one of claims 1-8, wherein said obtaining multiple sets of first detection parameters of the product includes:

Determine the statistical aggregation table;

According to the statistical aggregation table, the plurality of sets of first detection parameters of the product are obtained.
The method according to claim 9, wherein the statistical aggregation table is an HBase statistical aggregation table of Haidup database, and the determining of the HBase statistical aggregation table includes:

According to the Haidupu database, third detection parameters of multiple products are obtained from a detection device; the third detection parameters include the first detection parameters;

Perform data aggregation on the third detection parameters of the multiple products according to the structured query language SQL to determine the HBase statistical aggregation table.
The method according to any one of claims 1-10, wherein the plurality of first detection parameters include key process parameters and electro-permanent magnet EPM electrical parameters; the key process parameters include surface resistance RS parameters, At least one of the combination precision TP parameter, the line width CD parameter, the film thickness THK parameter, and the combination accuracy OL parameter; the electrical parameters include the threshold voltage VTH parameter, the mobility MOB parameter, the operating current ION parameter, and the reverse to at least one of the cut-off current IOFF parameters.
The method according to any one of claims 1-11, further comprising:

Before outputting the correlation evaluation values between the plurality of sets of second detection parameters, the correlation evaluation values between the plurality of sets of second detection parameters are sorted.
A detection parameter analysis device, including: an acquisition unit, a processing unit and an output unit;

The acquisition unit is configured to acquire multiple sets of first detection parameters of the product; the first detection parameters include detection parameters of multiple measurement points, and the measurement points are position points on the product;

The processing unit is configured to perform interpolation processing on the plurality of sets of first detection parameters according to an interpolation algorithm, and determine multiple sets of second detection parameters; the number of the second detection parameters is equal to the number of the first detection parameters. same;

The processing unit is further configured to determine correlation evaluation values between the plurality of sets of second detection parameters according to a correlation analysis algorithm; the correlation evaluation value is used to characterize all the parameters corresponding to each of the measurement points. Describe the correlation between multiple sets of second detection parameters;

The output unit is configured to output the correlation evaluation value of the second detection parameter.
A detection parameter analysis device, including a processor and a communication interface; the communication interface is coupled to the processor, and the processor is used to run computer programs or instructions to implement the method described in any one of claims 1-12 Detection parameter analysis method.
A detection parameter analysis system includes a detection parameter analysis device, and the detector parameter analysis device is used to execute the detection parameter analysis method described in any one of the above claims 1-12.
A detection parameter analysis application, wherein the detection parameter analysis application includes an application interactive interface. When a preset operation is performed on the application interactive interface, the detection parameter analysis application performs any of the above claims 1-12. The detection parameter analysis method described in one item.
A non-transitory computer-readable storage medium, wherein instructions are stored in the non-transitory computer-readable storage medium. When the computer executes the instructions, the computer executes any one of the above claims 1-12. The detection parameter analysis method described above.
A computer program product. The computer program product includes computer program instructions. When the computer program instructions are executed on a computer, the computer program instructions cause the computer to perform the detection parameter analysis as described in any one of claims 1-12. method.