CN117157541A

CN117157541A - Detection parameter analysis method and device

Info

Publication number: CN117157541A
Application number: CN202280000628.4A
Authority: CN
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: BOE Technology Group Co Ltd; Beijing Zhongxiangying Technology Co Ltd
Current assignee: BOE Technology Group Co Ltd; Beijing Zhongxiangying Technology Co Ltd
Priority date: 2022-03-30
Filing date: 2022-03-30
Publication date: 2023-12-01
Also published as: WO2023184281A1; WO2023184281A9

Abstract

A method and a device for analyzing detection parameters comprise the following steps: acquiring a plurality of groups of first detection parameters of a product, wherein the first detection parameters comprise detection parameters of a plurality of measurement points, and the measurement points are position points on the product; according to an interpolation algorithm, carrying out interpolation processing on a plurality of groups of first detection parameters, and determining a plurality of groups of second detection parameters, wherein the number of the second detection parameters is the same as that of the first detection parameters; according to a correlation analysis algorithm, determining correlation evaluation values among a plurality of groups of second detection parameters, wherein the correlation evaluation values are used for representing the correlation among the plurality of groups of second detection parameters corresponding to each measurement point; and outputting the correlation evaluation values among the plurality of groups of second detection parameters.

Description

Detection parameter analysis method and device

Technical Field

The present application relates to the field of data analysis, and in particular, to a method and apparatus for analyzing detection parameters.

Background

In the semiconductor and panel industries, there is a problem in that there are bad points on the produced products due to the influence of each production process or equipment. At present, after finishing some critical film layers of Glass, the critical parameters of the film layers are detected by a manual inspection mode.

However, because the detection process is complex and the data volume is huge, the defect causes are positioned by means of manual inspection, the processing time efficiency and the accuracy are limited, and the ever-increasing production demands are difficult to meet.

Disclosure of Invention

In one aspect, a method for analyzing a detection parameter is provided, the method comprising: obtaining a plurality of groups of first detection parameters of a product; the first detection parameters comprise detection parameters of a plurality of measurement points, and the measurement points are position points on the product; performing interpolation processing on a plurality of groups of first detection parameters according to an interpolation algorithm to determine a plurality of 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; determining a correlation evaluation value among a plurality of groups of second detection parameters according to a correlation analysis algorithm; the correlation evaluation value is used for representing the correlation among the plurality of groups of second detection parameters corresponding to each measurement point; and outputting the correlation evaluation values among the plurality of groups of second detection parameters.

In some embodiments, the interpolation algorithm described above is a Kriging interpolation; according to an interpolation algorithm, carrying out interpolation processing on a plurality of groups of first detection parameters to determine a plurality of groups of second detection parameters, wherein the method comprises the following steps: determining a coordinate distance between the first measurement point and the second measurement point and a half variance; the first measuring point and the second measuring point are measuring points in a plurality of measuring points corresponding to the same group of first detection parameters; determining the half variance of the plurality of predicted points according to the distance between the first measuring point and the second measuring point and the half variance; the predicted points are measurement points which do not have corresponding detection parameters in each group of first detection parameters in a plurality of measurement points corresponding to the groups of first detection parameters; determining a weight coefficient according to the half variances of the plurality of predicted points; determining target interpolation of a plurality of predicted points according to the weight coefficient and a plurality of groups of first detection parameters; and carrying out interpolation processing on a plurality of groups of first detection parameters according to target interpolation of a plurality of predicted points, and determining a plurality of groups of second detection parameters.

In some embodiments, determining the half variance of the plurality of predicted points according to the coordinate distance between the first measurement point and the second measurement point and the half variance includes: determining a semi-variance fitting curve according to the coordinate distance between the first measuring point and the second measuring point and the semi-variance; and determining the half variances of the plurality of predicted points according to the half variance fitting curve.

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

wherein d _ij Representing the coordinate distance between the first measurement point and the second measurement point, i representing the number of the first measurement point, x _i Representing the abscissa, y, of the first measuring point _i An ordinate representing a first measurement point, j representing the number of a second measurement point, x _j Representing the abscissa, y, of the second measuring point _j Representing the ordinate of the second measurement point.

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

wherein r is _ij Representing the half variance between the first measurement point and the second measurement point, E representing the covariance, z _i A detection parameter, z, representing a first measurement point _j Representing the detected parameter of the second measurement point.

The target interpolation of the plurality of predicted points satisfies the following formula:

Wherein,target interpolation, lambda, representing multiple predicted points _k Representing the weight coefficient, z _k Representing the detection parameter of a measurement point numbered k.

In some embodiments, the correlation analysis algorithm is Pearson correlation analysis, 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, Y each represents a second detection parameter, μ _X Mean town, mu, of the second detection parameter X _Y Represents the average value, sigma, of the second detection parameter Y _X Represents the standard deviation, sigma, of the second detection parameter X _Y Represents the standard deviation of the second detection parameter Y.

In some embodiments, the correlation analysis algorithm described above is the Kruskal-Wallis test method of Kruskal-vorax; determining a correlation evaluation value among the plurality of sets of second detection parameters according to a correlation analysis algorithm, including: sequencing a plurality of groups of second detection parameters according to the increasing sequence; determining the ranks of the ordered multiple groups of second detection parameters; determining statistics of a plurality of groups of second detection parameters according to the ranks of the plurality of groups of second detection parameters; and determining correlation evaluation values among the plurality of groups of second detection parameters according to the statistics of the plurality of groups of second detection parameters.

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

wherein H represents the statistics of the plurality of sets of second detection parameters, N represents the number of detection parameters included in the plurality of sets of second detection parameters, N represents the number of detection parameters included in one second detection parameter, R _X Representing the sum of the ranks of the second detection parameters X, R _Y Representing the sum of the ranks of the second detection parameters Y.

The correlation evaluation values among the plurality of sets of second detection parameters satisfy the following formula:

wherein P represents the correlation evaluation values among the plurality of sets of second detection parameters, H represents the statistics of the plurality of sets of second detection parameters, k represents the number of second detection parameters, Γ represents the gamma distribution function.

In some embodiments, the method further comprises: determining a contour map of a plurality of groups of second detection parameters; the contour map is used for representing the magnitude of detection parameters corresponding to each region on the product; and outputting a contour map of a plurality of groups of second detection parameters.

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

In some embodiments, the statistical aggregation table is a sea Du Pu database HBase statistical aggregation table, and determining the HBase statistical aggregation table includes: acquiring third detection parameters of the plurality of products from the detection device according to the sea Du Pu database; the third detection parameter comprises the first detection parameter; and carrying out data aggregation on third detection parameters of a plurality of products according to the structured query language SQL, and determining an HBase statistical aggregation table.

Acquiring a third detection parameter of the plurality of products from the detection device; the third detection parameter comprises the first detection parameter; determining an HBase statistical aggregation table according to third detection parameters of the plurality of products; and obtaining a first detection parameter of the product according to the HBase statistical aggregation table.

In some embodiments, the plurality of sets of first detection parameters include key process parameters and electro-permanent magnet EPM electrical parameters; the key technological parameters comprise at least one of surface resistance RS parameters, involution precision TP parameters, line width CD parameters, film thickness THK parameters and register precision OL parameters; the electrical parameters include at least one of a threshold voltage VTH parameter, a mobility MOB parameter, an operating current ION parameter, and a reverse off-current IOFF parameter.

In some embodiments, the method further comprises: the correlation evaluation values between the plurality of sets of second detection parameters are ordered before the correlation evaluation values between the plurality of sets of second detection parameters are output.

In another aspect, there is provided a parameter analysis apparatus including: the device comprises an acquisition unit, a processing unit and an output unit; an acquisition unit configured to acquire a plurality of sets of first detection parameters of a product; the first detection parameters comprise detection parameters of a plurality of measurement points, and the measurement points are position points on the product; the processing unit is configured to perform interpolation processing on a plurality of groups of first detection parameters according to an interpolation algorithm to obtain a plurality of groups of second detection parameters; the number of the second detection parameters is the same as the number of the first detection parameters; the processing unit is further configured to determine correlation evaluation values among a plurality of groups of second detection parameters according to a correlation analysis algorithm; the correlation evaluation value is used for representing the correlation among a plurality of groups of second detection parameters corresponding to each measuring point; and an output unit configured to output the correlation evaluation values of the plurality of sets of second detection parameters.

In some embodiments, the processing unit is further configured to determine a coordinate distance between the first measurement point and the second measurement point and a half variance; the first measuring point and the second measuring point are measuring points in a plurality of measuring points; the processing unit is further configured to determine the half variance of the plurality of predicted points according to the distance between the first measurement point and the second measurement point and the half variance; the processing unit is further configured to determine a weight coefficient according to the half variances of the plurality of predicted points; the processing unit is further configured to determine target interpolation of a plurality of predicted points according to the weight coefficient and the plurality of groups of first detection parameters; the processing unit is further configured to perform interpolation processing on the plurality of groups of first detection parameters according to target interpolation of the plurality of prediction points, and determine a plurality of groups of second detection parameters.

In some embodiments, the processing unit is further configured to determine a half variance fit curve from the coordinate distance between the first measurement point and the second measurement point and the half variance; and the processing unit is further configured to determine the half variances of the plurality of predicted points according to the half variance fitting curve.

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 ranks of the ordered multiple groups of second detection parameters; the processing unit is further configured to determine statistics of the plurality of groups of second detection parameters according to ranks of the plurality of groups of second detection parameters; the processing unit is further configured to determine correlation evaluation values among the plurality of sets of second detection parameters according to statistics of the plurality of sets of second detection parameters.

In some embodiments, the processing unit is further configured to determine a contour map of the plurality of sets of second detection parameters; the contour map is used for representing the magnitude of detection parameters corresponding to each region on the product; and the output unit is further configured to output a contour map of a plurality of groups of second detection parameters.

In some embodiments, the processing unit is further configured to determine a statistical aggregation table; the acquisition unit is further configured to acquire a plurality of groups of first detection parameters of the product according to the statistical aggregation table.

In some embodiments, the obtaining unit is further configured to obtain a third detection parameter of the plurality of products from the detection device according to the sea Du Pu database; the third detection parameter comprises the first detection parameter; and the processing unit is further configured to perform data aggregation on the third detection parameters of the plurality of products according to the structured query language SQL, and determine an HBase statistical aggregation table.

In some embodiments, the plurality of sets of first detection parameters includes key process parameters and electro-permanent magnet EPM electrical parameters; the key technological parameters comprise at least one of surface resistance RS parameters, involution precision TP parameters, line width CD parameters, film thickness THK parameters and register precision OL parameters; the electrical parameters include at least one of a threshold voltage VTH parameter, a mobility MOB parameter, an operating current ION parameter, and a reverse off-current IOFF parameter.

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

In yet another aspect, a detection parameter analysis application is provided, where the detection parameter analysis application includes an application interaction interface that, when performing a preset operation on the application interaction interface, causes the detection parameter analysis application to perform the parameter analysis apparatus method according to any one of the embodiments described above.

In yet another aspect, a computer-readable storage medium is provided. The computer readable storage medium stores computer program instructions that, when run on a computer (e.g., a parameter analysis device), cause the computer to perform the parameter analysis device method of any of the embodiments described above.

In yet another aspect, a computer program product is provided. The computer program product comprises computer program instructions which, when executed on a computer (e.g. a parameter analysis device), cause the computer to perform the parameter analysis device method as described in any of the embodiments above.

In yet another aspect, a computer program is provided. The computer program, when executed on a computer (e.g. a parameter analysis device), causes the computer to perform the parameter analysis device method as described in any of the embodiments above.

Drawings

In order to more clearly illustrate the technical solutions of the present disclosure, the drawings that need to be used in some embodiments of the present disclosure will be briefly described below, and it is apparent that the drawings in the following description are only drawings of some embodiments of the present disclosure, and other drawings may be obtained according to these drawings to those of ordinary skill in the art. Furthermore, the drawings in the following description may be regarded as schematic diagrams, not limiting the actual size of the products, the actual flow of the methods, the actual timing of the signals, etc. according to the embodiments of the present disclosure.

Fig. 1 is a schematic view of an application scenario of a parameter analysis method according to some embodiments;

FIG. 2 is a flow chart of a method of parameter analysis provided in accordance with some embodiments;

FIG. 3 is an application interaction interface provided in accordance with some embodiments;

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

FIG. 5 is a flow chart of another method of parameter analysis provided in accordance with some embodiments;

FIG. 6 is a flow chart of another method of parameter analysis provided in accordance with some embodiments;

FIG. 7 is a flow chart of another method of parameter analysis provided in accordance with some embodiments;

FIG. 8 is a contour diagram provided in accordance with some embodiments;

FIG. 9 is another contour diagram provided in accordance with some embodiments;

FIG. 10 is a block diagram of a parameter analysis device provided in accordance with some embodiments;

fig. 11 is a block diagram of another parameter analysis device provided in accordance with some embodiments.

Detailed Description

The following description of the embodiments of the present disclosure will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by one of ordinary skill in the art based on the embodiments provided by the present disclosure are within the scope of the present disclosure.

Throughout the specification and claims, unless the context requires otherwise, the word "comprise" and its other forms such as the third person referring to the singular form "comprise" and the present word "comprising" are to be construed as open, inclusive meaning, i.e. as "comprising, but not limited to. In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example", "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 may be combined in any suitable manner in any one or more embodiments or examples.

The terms "first" and "second" are used below for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the embodiments of the present disclosure, unless otherwise indicated, the meaning of "a plurality" is two or more.

At least one of "A, B and C" has the same meaning as at least one of "A, B or C," both include the following combinations of A, B and C: a alone, B alone, C alone, a combination of a and B, a combination of a and C, a combination of B and C, and a combination of A, B and C.

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

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

The use of "adapted" or "configured to" herein is meant to be an open and inclusive language that does not exclude devices adapted or configured to perform additional tasks or steps.

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

As used herein, "about," "approximately" or "approximately" includes the stated values as well as average values within an acceptable deviation range of the particular values as determined by one of ordinary skill in the art in view of the measurement in question and the errors associated with the measurement of the particular quantity (i.e., limitations of the measurement system).

As used herein, "parallel", "perpendicular", "equal" includes the stated case as well as the case that approximates the stated case, the range of which is within an acceptable deviation range 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 (i.e., limitations of the measurement system). For example, "parallel" includes absolute parallel and approximately parallel, where the acceptable deviation range for approximately parallel may be, for example, a deviation within 5 °; "vertical" includes absolute vertical and near vertical, where the acceptable deviation range for near vertical may also be deviations within 5 °, for example. "equal" includes absolute equal and approximately equal, where the difference between the two, which may be equal, for example, is less than or equal to 5% of either of them within an acceptable deviation of approximately equal.

It will be understood that when a layer or element is referred to as being "on" another layer or substrate, it can be directly on the other layer or substrate, or intervening layers may also be present between the layer or element and the other layer or substrate.

The following explains terms related to the embodiments of the present disclosure, for convenience of the reader.

(1) Sea Du Pu database (HBase) statistical aggregation table

HBase is a distributed storage system that stores structured data. HBase is a distributed mass listed non-relational database, i.e. the data in HBase is stored on a column-family basis, one column-family containing several columns. HBase may be used when real-time read-write, random access of a very large data set is required.

The HBase statistical aggregation table is a table for statistical data obtained by performing aggregation operation by using a mature computer language such as a structured query language (structured query language, SQL) and the like based on a data set stored in the HBase.

Correspondingly, based on the update of the data source, the HBase statistics aggregation table can also be synchronously updated. Illustratively, in some embodiments provided by the present disclosure, HBase statistics aggregation tables store various detection parameters about the product, such as key process parameters and electro permanent magnet (electro permanent magnet, EPM) electrical parameters, obtained from the production facility. Key process parameters may include: a sheet resistance (resistance surface, RS) parameter, a Total Pitch (TP) parameter, a line width (criticlal dimension, CD) parameter, a Thickness (THK) parameter, and a fitting accuracy OL parameter. The EPM electrical parameters may include: a threshold voltage (voltage of threshold, VTH) parameter, a Mobility (MOB) parameter, an operating current (denoted ION) parameter, and a reverse off current (denoted IOFF) parameter. And, every a certain preset time (the preset time can be set manually), the HBase statistical aggregation table updates the detection parameters stored by the HBase statistical aggregation table according to the update of the detection parameters of the production equipment.

(2) Interpolation algorithm

Interpolation algorithm is an important approach to discrete function approximation, and can be used to estimate the approximation of the function at other points by the value condition of the function at a limited number of points.

In the mathematical field, interpolation refers to interpolating a continuous function on the basis of discrete data such that the continuous curve passes through all given discrete data points. Interpolation is an important method of discrete function approximation, by which the approximation of a function at other points can be estimated from the value condition of the function at a limited number of points. In the image field, interpolation is used to fill in gaps between pixels when an image is transformed.

For example, kriging (Kriging) interpolation is one of the interpolation algorithms that are more commonly used in the field of data analysis.

Illustratively, in some embodiments provided in the present disclosure, the parameter analysis device can interpolate a plurality of different types of detection parameters acquired from the measurement points by using a Kriging interpolation method, so as to ensure uniformity of the plurality of types of detection parameters, so as to avoid negative influence on correlation analysis of the detection parameters due to different distribution and number of the measurement points.

(3) Contour map

The contour map is generally applied to the fields of geographic exploration and map drawing, namely, points with the same ground surface height are connected into a loop line which is directly projected to a plane to form a horizontal curve, and the loop lines with different heights cannot be combined.

In the field of geographic technology, contour maps are commonly used. In short, the contour map is that points with the same height of the ground surface are connected into a loop line to be directly projected to a plane to form a horizontal curve, the loop lines with different heights cannot be combined unless the ground surface displays cliffs or cliffs, so that lines at a certain place are too densely overlapped, if the ground surface is a flat and open hillside, the distance between the curves is quite wide, the datum line of the contour map is based on the average sea tide level of the sea surface, and a manufacturing mark description is arranged below each map, so that a user can conveniently use the contour map, wherein the main diagram is provided with a scale, a figure number, a picture engagement table, a legend and an azimuth offset angle.

In some embodiments provided in the present disclosure, the parameter analysis device is based on detection parameters detected at measurement points of different areas of a product, and after interpolation processing is performed on the detection parameters, a contour map is drawn according to the detection parameters and the detection parameters, so as to intuitively represent the sizes of the detection parameters of the different areas of the product, so as to assist a staff in analyzing the correlation analysis results of the detection parameters.

(4) Correlation analysis algorithm

The correlation analysis algorithm is an algorithm for analyzing two or more variable elements with correlation so as to measure the correlation degree of two variable factors.

Correlation analysis refers to analyzing two or more variable elements with correlation, so as to measure the correlation degree of two variable factors. There is a certain association or probability between elements of the correlation to be able to perform the correlation analysis. The relevance is not equal to causality, is not simple personalized, and the scope and the field covered by the relevance almost cover the aspects seen by us, and the definition of the relevance in different disciplines also has great difference.

There is a certain association or probability between elements of the correlation to be able to perform the correlation analysis.

For example, the Pearson (Pearson) algorithm, the Kruskal-Wallist (K-W) test, and the Mann-Whitney (M-W) rank sum test are algorithms that are more commonly used in the field of data correlation analysis.

Illustratively, in some embodiments provided by the present disclosure, the parameter analysis device may perform correlation analysis on the detection parameters of the various categories of the product subjected to interpolation processing according to the Pearson algorithm and the K-W test method, so that a worker determines a defective area of the product according to the correlation analysis result, and performs process improvement or equipment obstacle removal.

The foregoing has described terms involved in the embodiments of the present disclosure.

In the field of the present semiconductor and panel industries, in the field of the film transistor liquid crystal display (thin film transistor liquid crystal display, TFT-LCD), the products of the TFT-LCD type are produced in poor conditions. The occurrence of the defect may be caused by any manufacturing process or equipment in the entire production line. The product's failure can often manifest itself in terms of some key parameters of the product.

Therefore, at present, after some key film layers of the product are manufactured, key parameters of the product are detected. The staff can judge whether the product is poor or not and cause of the poor through analyzing the key parameters in a manual checking mode, and then the staff can improve the manufacturing process or troubleshoot the manufacturing equipment according to the cause of the poor. However, because the manufacturing process of the product is complicated and huge in quantity, the reasons of the defects are positioned by means of manual inspection, the timeliness and the accuracy are extremely limited, and the ever-increasing production demands are difficult to meet.

In the prior art, two schemes are provided for analysis of bad causes of products:

Scheme one, a defect mode analysis method (CN 112184691A) based on bad Map, the scheme arranges the defect measurement results of the same product from different sources and various characteristic measurement values of the product into coordinate data information associated with Map coordinates of a display panel according to a certain standard aiming at a certain product type. And establishing density clustering models of the bad data information and the display panel data for different product types by taking the position information of the bad coordinates of the display panel as an analysis object, wherein the clustering type depends on the defect information of a corresponding display panel production tool and the correlation degree of the product characteristics and the bad coordinates, judging the similarity between the bad information of each product and the type of the corresponding density cluster through the similarity coefficient of the density clustering model, and screening out effective bad types. In summary, the approach quickly locates the bad information into bad types.

According to the scheme II, the bad root path analysis method and the system (CN 111932394A) based on the association rule mining can rapidly and automatically filter a large number of site equipment without suspicious performance, automatically reduce the analysis range and avoid additional input of experience knowledge and manual intervention; and the invention traverses all possible site equipment combinations, sorts according to ascending degree descending order, automatically highlights the most suspicious combinations in a large number of possible path combinations, and can assist staff in quickly positioning site equipment paths causing bad root cause. Summarizing, the scheme traverses all possible equipment path combinations based on an improved association rule mining algorithm, and automatically and quickly locates bad root causes.

Because the abnormality of the correlation between the detection parameters (such as the electrical parameter and the key process parameter) of the liquid crystal display (liquid crystal display, LCD) and the organic light-emitting diode (OLED) products directly causes the product to appear bad, and the types of the bad products appearing in the abnormal degree of the correlation between the detection parameters are different, it is extremely necessary to quantify the correlation between the detection parameters into indexes, and the bad cause of the preamble station can be quickly located, so that the service personnel can efficiently and timely adjust the detection parameters to carry out verification test and maintenance. However, neither of the above schemes involves correlation between the detected parameters, and does not enable rapid localization to the bad root cause of the preamble station.

In view of this, the disclosure provides a parameter analysis method and apparatus, which are used to solve the problem that in the prior art, when analyzing the detection parameters of the product, the processing time limit and the accuracy are limited, and it is difficult to meet the increasing production demands. The method provided by the disclosure can also quantify the correlation among the parameters into indexes, and rapidly locate the bad root cause of the preamble station, so that service personnel can efficiently and timely adjust the parameters for verification test and maintenance.

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

the processor may be a general purpose central processing unit (central processing unit, CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs in accordance with the present disclosure.

The transceiver may be a device using any transceiver or the like for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), etc.

Memory, which may be, but is not limited to, read-only memory (ROM) or other type of static storage device that may store static information and instructions, random access memory (random access memory, RAM) or other type of dynamic storage device that may store information and instructions, but may also be electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), compact disc read-only memory (compact disc read-only memory) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be stand alone and be coupled to the processor via a communication line. The memory may also be integrated with the processor.

It should be noted that, the embodiments of the present disclosure may refer to or refer to each other, for example, the same or similar steps, and the method embodiment, the system embodiment and the apparatus embodiment may refer to each other, which is not limited.

The implementation of the disclosed embodiments will be described in detail below with reference to the attached drawings.

Fig. 1 is a schematic view of an application scenario of a parameter analysis method according to some embodiments, as shown in fig. 1. In the application scenario of fig. 1, a parameter analysis device 10 and a production facility 20 are included.

The parameter analysis device 10 is used for acquiring detection parameters from the production equipment 20 and performing parameter analysis, so that service personnel can perform verification test and maintenance on the production equipment 20 according to the result of the parameter analysis.

Production equipment 20 for producing the product. And in the production apparatus 20, there are provided a plurality of types of inspection stations. A sensing station is used to measure a class of sensed parameters of a product. Accordingly, the production facility 20 acquires the detection parameters of the product according to the detection station set up by itself, and transmits these detection parameters to the parameter analysis device 10.

For example, in practical application, for a production line for producing products in a factory, the parameter analysis device 10 gathers detection parameters collected by a detection station set by a production device 20 set on the production line, performs parameter analysis and outputs a parameter analysis result, so that a worker can remotely determine a cause of a defect of the product through the analysis result, and further improve the production process or repair the production device 20 on the production line.

As shown in fig. 2, fig. 2 is a parameter analysis method provided according to some embodiments, the method including the steps of:

step 201, a parameter analysis device obtains multiple groups of first detection parameters of a product.

The product can be a panel, a plane module or a 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), a plasma display screen (plasma display panel, PDP).

The detection parameters of the product can be divided into key process parameters and EPM electrical parameters. The key process parameters include at least one of the following: surface resistance RS parameters, overlay accuracy TP parameters, line width CD parameters, film thickness THK parameters, and overlay accuracy OL parameters. The EPM electrical parameters include at least one of the following: a threshold voltage VTH parameter, a mobility MOB parameter, an operating current ION parameter, and a reverse off-current IOFF parameter. Accordingly, the first sensed parameter comprises at least one of the critical process parameters and at least one of the EPM electrical parameters, i.e. the set of first sensed parameters comprises the same critical process parameter or EPM electrical parameter.

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

Illustratively, the parameter analysis device may acquire the reverse cutoff current IOFF parameter and the film thickness THK parameter of different measurement points on the Class, and take the acquired reverse cutoff current IOFF parameter and film thickness THK parameter as the first detection parameter of the Class; alternatively, the parameter analysis device may acquire the reverse off current IOFF parameter and the surface resistance RS parameter at different measurement points on the Class, and use the acquired reverse off current IOFF parameter and surface resistance RS parameter as the first detection parameter of the Class.

It should be understood that the measurement point is a position point selected on the product for measuring the detection parameter when the parameter analysis device acquires the detection parameter of the product. Typically the number of measurement points is between 20 and 180 for a piece of product.

In one possible implementation, the parameter analysis means obtains a plurality of sets of first detection parameters of the product from the statistical aggregation table. Wherein, as described 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 a third inspection parameter of the plurality of products from the inspection apparatus, the third inspection parameter of the products including all types of critical 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 may acquire third detection parameters of the plurality of products from the detection apparatus according to the HBase. The detection equipment is equipment which is arranged on production equipment of the product and used for acquiring detection parameters of the product.

Further, the parameter analysis device performs data aggregation on third detection parameters of the plurality of products according to SQL, stores the third detection parameters in a table form and determines an HBase statistical aggregation table.

Wherein the third detection parameter comprises the first detection parameter. Further, the HBase statistical aggregation table determined according to the third detection parameter includes a 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 detection requirement of the user.

The detection requirement of the staff member can be used for indicating the first detection parameters required by the user detection, the detection requirement comprises identification information of the first detection parameters to be detected and other information, such as screening conditions of the first detection parameters, and the first detection parameters meeting the screening conditions can be used for detection analysis. Optionally, the staff may send the detection requirement to the parameter analysis device through the application interaction interface, so that the parameter analysis device obtains the first detection parameter of the product from the HBase statistics aggregation table according to the detection requirement.

As shown in fig. 3, the parameter analysis device provides an application interactive interface, on which a worker can set the acquiring conditions of the first detection parameters, and after the acquiring conditions of the first detection parameters are set, the worker performs a confirmation operation, so as to acquire multiple groups of first detection parameters from the data stored in the HBase statistics aggregation table. For example, the staff may set the first detection parameters of type IOFF1_20 (i.e., IOFF parameters) and THICKNESS (i.e., THK parameters) for detection site numbers 990G and 576K for Class ID 1, with type BNA650QU5V402, with factory number aray between 2021, 7, 1 and 2021, 7, 22.

Optionally, the parameter analysis device stores the multiple groups of first detection parameters in the form of a table after obtaining the multiple groups of first detection parameters of Glass from the HBase statistical aggregation table according to conditions set by a worker, as shown in the following table 1:

TABLE 1 data sheet of selected Class ID1 IOFF parameters and THK parameters

Wherein Step represents the station number of the detection station, item represents the type of the detection parameter, x and y represent the abscissa and ordinate of the measurement point, respectively, and Value represents a specific Value of the detection parameter.

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

Wherein the interpolation algorithm is an interpolation algorithm. The interpolation algorithm may be, for example, kriging interpolation, or other interpolation algorithms, and the disclosure is not particularly limited. The specific implementation process of interpolating the plurality of groups of first detection parameters according to the Kriging interpolation method may be referred to as the following fig. 5, and will not be described herein.

The number of sets of the second detection parameter is the same as the number of sets of the first detection parameter. That is, before and after the interpolation process, the number of categories of the detection parameters is not changed, and the second detection parameters are only the first detection parameters corresponding to the second detection parameters, which are obtained after the interpolation process is performed according to the interpolation algorithm. Compared with the first detection parameters, the second detection parameters are added with the parameter values of some predicted points.

The predicted points are all the measurement points corresponding to the plurality of groups of first detection parameters, and the measurement points with the corresponding detection parameters in each group of first detection parameters are not included. For example, taking two sets of first detection parameters, i.e., IOFF parameters and THK parameters, as an example, the parameter analysis device divides the Class into grid points of 16×14, and at this time, the interpolation process has 224 corresponding location points, i.e., the interpolation process is aimed at making the IOFF parameters and THK parameters have parameter values of 224 identical location points.

At this time, for the IOFF parameter, the selection of the predicted point can be divided into two cases:

in case one, for some measurement points, the THK parameter includes detection parameters of the measurement points, and the IOFF parameter does not include the measurement points are used as prediction points when interpolation processing is performed on the IOFF parameter.

In the second case, if the IOFF parameter and the THK parameter do not include the detection parameters of the measurement points, the measurement points are used as prediction points when the interpolation processing is performed on the IOFF parameter.

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

in case three, for some measurement points, the IOFF parameter includes detection parameters of the measurement points, and the THK parameter is not included, and the measurement points are used as prediction points when interpolation processing is performed on the THK parameter.

In the fourth case, for some measurement points, neither the THK parameter nor the IOFF parameter includes the detection parameters of these measurement points, and these measurement points are used as prediction points when interpolation processing is performed on the THK parameter.

It should be understood that for a set of first detection parameters, the predicted points are only the location points for which the interpolation algorithm chooses for that set of first detection parameters to perform the interpolation calculation. The parameter values of the predicted points are calculated according to an interpolation algorithm, and the parameter analysis device does not actually measure the set of first detection parameters at the predicted points.

In this way, the parameter value of the newly added predicted point is combined with the parameter value obtained by actually detecting the measurement point included in the first detection parameter to form a second detection parameter.

Illustratively, in connection with the example in step 201, the parameter analysis means divides the Class into grid points of 16×14, one grid point being a measurement point or prediction point, one measurement point or prediction point corresponding to one or more sets of detection parameters. The parameter analysis device is assumed to acquire two groups of first detection parameters of Class, namely an IOFF parameter and a THK parameter. The number of measurement points of the IOFF parameter is 100, that is, the IOFF parameter includes the IOFF values of 100 location points. There are 140 measurement points of the THK parameter, that is, the THK parameter includes THK values of 140 location points in total. Some of the measurement points corresponding to the IOFF parameter and the THK parameter are the same, and some of the measurement points are different. At this time, interpolation processing is required to be performed on the IOFF parameter and the THK parameter according to the interpolation algorithm, so as to ensure uniformity of the IOFF parameter and the THK parameter, and facilitate subsequent analysis of correlation.

Further, the parameter analyzing device performs two sets of first detection parameters: after interpolation processing is performed on the IOFF parameters and the THK parameters, the IOFF parameters include IOFF values of 224 location points, wherein the IOFF values of 100 location points are the actually detected IOFF values of the measurement points, and the IOFF values of 124 location points are the newly added IOFF values after interpolation processing is performed according to an interpolation algorithm. The same applies to the THK parameters, wherein in the THK parameters subjected to interpolation processing, the THK values of 140 position points are the IOFF values of the actually detected measuring points, and the THK values of 84 position points are the newly added THK values after interpolation processing according to an interpolation algorithm. After that, the parameter analysis means takes the IOFF parameter and the THK parameter each including the parameter values of 224 position points after the interpolation processing as a plurality of sets of second detection parameters. It will be appreciated that for all the location points included in the two sets of first detection parameters (i.e. 224 location points described above), each location point has a corresponding IOFF parameter value and THK parameter value, so that the uniformity of the two first detection parameters after interpolation is improved.

Step 203, the parameter analysis device determines correlation evaluation values among a plurality of groups of second detection parameters according to a correlation analysis algorithm.

Wherein the correlation analysis algorithm is a correlation analysis algorithm. The correlation evaluation values between the sets of second detection parameters may be used to characterize the degree of correlation between the sets of second detection parameters. Illustratively, the correlation analysis algorithm may be at least one of a Pearson algorithm and a K-W test. The specific implementation of the Pearson algorithm may be as follows from equation (4) to equation (5). The specific implementation of the K-W test method can be referred to as shown in fig. 6 below, and will not be described here again.

It should be noted that, the parameter analysis device may determine the correlation evaluation values between the plurality of sets of second detection parameters according to a plurality of correlation analysis algorithms at the same time. Accordingly, each correlation analysis algorithm has a corresponding correlation evaluation value, a worker can study the process problems represented by the detection parameters of the Class according to the correlation evaluation values obtained by the algorithms, and compared with the process problems of the Class which are evaluated according to the correlation evaluation value obtained by one algorithm, the process problems of the Class which are evaluated by using the correlation evaluation algorithms can ensure the accuracy and the efficiency of process improvement or equipment maintenance.

Illustratively, in conjunction with the example in step 202, the second detection parameters include two of IOFF parameters and THK parameters, both of which are interpolated. The parameter analysis device carries out correlation analysis on the IOFF parameter and the THK parameter according to a correlation analysis algorithm to obtain a correlation evaluation result of the IOFF parameter and the THK parameter, and for each measuring point, the correlation evaluation value is used for representing the correlation degree of the IOFF parameter and the THK parameter above the measuring point.

Step 204, the parameter analysis device outputs correlation evaluation values among a plurality of groups of second detection parameters.

Alternatively, the parameter analysis means may output the correlation evaluation values among the plurality of sets of the second detection parameters in a plurality of ways. The correlation evaluation value is used for representing the correlation among the plurality of groups of second detection parameters corresponding to each measurement point.

In one possible implementation, as shown in fig. 4, the parameter analysis means outputs the correlation evaluation values between the plurality of sets of second detection parameters in the form of a line graph.

Optionally, before outputting the correlation evaluation values between the plurality of sets of second detection parameters, the parameter analysis device ranks the correlation evaluation values between the plurality of sets of second detection parameters, so that a worker can intuitively see the degree of correlation between the detection parameters corresponding to each measurement point.

After acquiring the correlation evaluation values among the plurality of sets of second detection parameters output by the parameter analysis device, the staff can determine the defective area of the product according to the correlation evaluation values.

Illustratively, 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 a preset threshold value 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, it is assumed that the correlation evaluation value of the IOFF parameter and the THK parameter at a certain measurement point is 0.7, and since 0.7 is greater than 0.65, the process evaluation result at this measurement point is good.

After that, the worker determines the set of measurement points for which the process evaluation result is poor as a process poor region of the product.

Based on the technical scheme, the parameter analysis device disclosed by the invention can determine the interpolated detection parameters of various types by acquiring the detection parameters of various types of products and carrying out interpolation processing on each type of detection parameters through an interpolation algorithm, so that the data uniformity among each type of detection parameters can be maintained through the interpolation processing, and support is provided for subsequent correlation analysis; and then, the parameter analysis device carries out correlation evaluation on the interpolated detection parameters according to a correlation analysis algorithm to determine a specific correlation evaluation value. Therefore, the relativity between the detection parameters is quantified, and workers can rapidly locate the bad areas reflected by the detection parameters of the detection sites, so that the workers can efficiently and timely adjust the parameters to carry out verification test and maintenance, the processing timeliness and the accuracy are effectively improved, and the ever-increasing production demands can be met.

As one possible embodiment of the disclosure, in conjunction with fig. 2, as shown in fig. 5, when the interpolation algorithm is Kriging interpolation, the step 202 specifically includes the following steps:

Step 501, a parameter analysis device determines a coordinate distance between a first measurement point and a second measurement point and a half variance.

The first detection parameter includes detection parameters for a plurality of measurement points. Illustratively, taking a product as a Class example, the first detection parameters include IOFF parameters and THK parameters. At this time, the IOFF parameter includes IOFF values of a plurality of measurement points above the Class, and the THK parameter includes THK values of a plurality of measurement points above the Class.

The first measuring points and the second measuring points are measuring points in a plurality of measuring points corresponding to the same group of first detection parameters. The first detection parameter includes an IOFF parameter and a THK parameter, and 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 any two measurement points corresponding to the THK parameter.

In one possible embodiment, the parameter evaluation device first determines the coordinates of the first measuring point and the second measuring point, and then calculates the distance between the first measuring point and the second measuring point. Illustratively, calculating the distance between the first measurement point and the second measurement point satisfies the following equation 1:

In one possible implementation, the parameter analysis device first calculates the covariance between the first measurement point and the second measurement point, and calculates the half-variance between the first measurement point and the second measurement point based on this. Illustratively, calculating the half variance between the first measurement point and the second measurement point satisfies the following equation 2:

wherein r is _ij Representing the half variance between the first measurement point and said second measurement point, E representing the covariance, z _i A detection parameter, z, representing a first measurement point _j Representing the detected parameter of the second measurement point.

Step 502, the parameter analysis device determines the half variance of the plurality of predicted points according to the distance between the first measurement point and the second measurement point and the half variance.

The parameter analysis device determines the point positions in a specific neighborhood range of the measurement points corresponding to the plurality of groups of first detection parameters or the specific number of adjacent point positions as a plurality of predicted points.

Illustratively, two first detection parameters exist, namely an IOFF parameter and a THK parameter, corresponding to the examples in step 202 described above. The number of measurement points of the IOFF parameter is 100, that is, the IOFF parameter includes 100 points of IOFF values, and the number of measurement points of the THK parameter is 140, that is, the THK parameter includes 140 points of THK values. Now, interpolation processing is required for the IOFF parameter, so that the IOFF parameter includes parameter values of 224 points, and similarly, THK parameter values. 124 points corresponding to the newly added parameter value in the IOFF parameter and 84 points corresponding to the newly added parameter value in the THK parameter are predicted points.

In one possible implementation, the parameter analysis means determines a half-variance fit curve from the coordinate distance between the first measurement point and the second measurement point and the half-variance. After that, the parameter analysis device determines the half variances of the plurality of predicted points according to the half variance fitting curve.

It should be noted that, the above-mentioned half variance fitting curve is obtained by the parameter analysis device drawing the distances and half variances corresponding to all the measurement points obtained by calculation into a scatter diagram after the calculation of the distances and half variances between all possible arbitrary two points in the measurement points corresponding to the plurality of groups of first detection parameters is completed, and finding an optimal curve for fitting.

Further, the parameter analyzing means may obtain a function expression r=r (d) of the semi-variance fitting curve. It will be understood that the specific expression of the function expression r=r (d) is determined by a fitted curve, and will be different according to the values of the multiple sets of first detection functions, which will not be described in detail in this embodiment. Thus, the parameter analysis device can determine the half variance corresponding to each predicted point from the coordinates of the predicted point.

In step 503, the parameter analysis device determines a weight coefficient according to the half variances of the plurality of predicted points.

The weight coefficient is used for carrying out weighted summation on parameter values of all measurement points included by the plurality of groups of first detection parameters so as to determine target interpolation of the predicted points. The weighting coefficient lambda here _k Satisfies the estimated valueAnd the true value z ₀ A set of optimal coefficients with the smallest difference, i.eAt the same time meet the condition of unbiased estimationThat is, the covariance between the target interpolation of the predicted point calculated by the parameter analysis device and the actual value of the predicted point is 0.

In the Kriging interpolation method, the weight coefficient is determined by a half variance function r=r (d) of the half variance fitting curve in step 502. Determining the weighting factor lambda in particular from the half variance function r=r (d) _k The method of (1) can refer to a Kriging interpolation method, and the embodiments of the disclosure are not described in detail.

Step 504, the parameter analysis device determines target interpolation of a plurality of predicted points according to the weight coefficient and the plurality of groups of first detection parameters.

Optionally, after the weight coefficient is calculated, the parameter analysis device multiplies the detection parameter of each measurement point included in the multiple groups of first detection parameters by the corresponding weight coefficient and sums the detection parameters to obtain target interpolation of multiple prediction points.

In one possible implementation, the target interpolation for the plurality of predicted points satisfies the following equation 3:

wherein,target interpolation, lambda representing the plurality of predicted points _k Representing the weight coefficient, z _k Representing a detection parameter of said measuring point numbered k.

Step 505, the parameter analysis device interpolates multiple groups of first detection parameters according to target interpolation of multiple predicted points, and determines multiple groups of second detection parameters.

Illustratively, corresponding to the example in step 202, the product is a Class, and the parameter analysis device divides the Class into grid points of 16×14, and interpolates the IOFF parameter and the THK parameter with one grid point as one measurement point or predicted point. That is, after performing interpolation processing, the IOFF parameter includes IOFF values of 224 points, where the IOFF values of 100 points are the IOFF values of the actually detected measurement points, and the IOFF values of 124 points are the newly added IOFF values after performing interpolation processing on the IOFF parameter by the parameter analysis device according to target interpolation of a plurality of predicted points. The same applies to the THK parameters, and in the THK parameters subjected to interpolation processing, the THK values of 140 points are IOFF values of the actually detected measurement points, and the THK values of 84 points are the THK values newly added after the parameter analysis device performs interpolation processing on the THK parameters according to target interpolation of a plurality of predicted points.

After that, the parameter analysis means takes the IOFF parameter and the THK parameter each including the parameter values of 224 points after the interpolation processing as a plurality of sets of second detection parameters.

Based on the technical scheme, the parameter analysis device disclosed by the disclosure carries out interpolation processing on multiple types of detection parameters through a Kriging interpolation method, and the interpolation processing in the step can keep the data uniformity among each detection parameter, so that the correlation among the detection parameters of the product can be conveniently evaluated in the subsequent step, and the accuracy of the correlation evaluation is improved.

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

The Pearson correlation coefficient is a method for measuring the similarity of data, and is used for describing the trend that the data of two groups of data change and move together, and is a value between-1 and 1. When the linear relation of the two groups of data is enhanced, the correlation coefficient tends to be-1 or 1; when one variable increases and the other variable also increases, the positive correlation between the two variables is indicated, and the correlation coefficient is larger than 0; when one variable increases and the other decreases, indicating a negative correlation between them, 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.

Illustratively, the calculation formula of the Pearson correlation analysis is expressed as the covariance of two variables divided by the standard deviation of the two variables. In connection with the example of step 202, in the embodiment of the present disclosure, the correlation evaluation values among the plurality of sets of second detection parameters satisfy the following equation 4:

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

in connection with the example of step 202, X may be represented as n detected values (X ₁ ,X ₂ ,…,X _n ) Y may be expressed as n detected values (Y ₁ ,Y ₂ ,…,Y _n )。

The correlation analysis algorithm is described above as the Pearson correlation analysis method, and the parameter analysis device can determine the correlation evaluation values among a plurality of groups of second detection parameters through the algorithm, so that a worker can determine the bad areas of products according to the correlation analysis result, and process improvement or equipment obstacle removal can be performed.

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

step 601, the parameter analysis device orders a plurality of groups of second detection parameters according to the increasing order.

For example, in combination with the example in step 202, there are two first detection parameters, i.e., IOFF parameter and THK parameter, respectively, and the IOFF parameter is represented by sample X and the THK parameter is represented by sample Y, and then both sample X and sample Y include n parameter values, in this example, n=16×14=224. After that, the parameter analysis device ranks all N (n= 2*n) parameter values in ascending order.

Step 602, the parameter analysis device determines the ranks of the ordered multiple groups of second detection parameters.

Optionally, the parameter analysis means sums the ranks of each of the second detection parameters after determining the ranks of the ordered sets of second detection parameters.

Illustratively, in connection with the example in step 601, the sum of the ranks of IOFF parameters in the ordering is denoted by Rx and the sum of the ranks of THK parameters in the ordering is denoted by Ry.

Step 603, the parameter analysis device determines statistics of multiple groups of second detection parameters according to the ranks of multiple groups of second detection parameters.

Illustratively, in connection with the example in step 602, the statistics satisfy the following equation 6:

Step 604, the parameter analysis device determines correlation evaluation values among the plurality of groups of second detection parameters according to statistics of the plurality of groups of second detection parameters.

Illustratively, in connection with the example in step 603, the correlation evaluation values among the sets of second detection parameters satisfy the following equation 7:

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

The above description is made when the correlation analysis algorithm is a K-W test method, and by using the algorithm, the parameter analysis device can determine correlation evaluation values among a plurality of groups of second detection parameters, so that a worker can determine a defective area of a product according to a correlation analysis result, and perform process improvement or equipment obstacle removal.

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

The main idea of the M-W rank sum test method is to assume that two parameter samples involved in the analysis are respectively from two identical populations except for the population mean, in order to test whether the mean of the two populations differs significantly. The specific algorithm steps of the M-W rank sum test method are as follows:

the first step: the two sets of data are mixed and ranked in order of size. The smallest data rank is 1, the second smallest data rank is 2, and so on (if there is an equality of data, the average of these several data ranks is taken as its rank).

And a second step of: respectively find the class sum W of two samples ₁ 、W ₂ 。

And a third step of: calculating M-W rank sum test statistic U of two parameter samples ₁ And U ₂ 。

Illustratively U ₁ The following equation 8 is satisfied:

illustratively U ₂ The following equation 9 is satisfied:

wherein n is ₁ For the amount of the first sample, n ₂ Is the second sample amount.

Further, select U ₁ And U ₂ Minimum of (3) and threshold value U _a Comparing when U<U _a When the two parameter samples are considered to be relatively related; when U is greater than U _a When two parameter samples are considered uncorrelated. Illustratively U _a Typically a value of 0.05 is chosen.

The above description is given to the correlation analysis algorithm being the M-W rank sum test method, by which the parameter analysis device can determine the correlation evaluation values among the plurality of groups of second detection parameters, so that the staff can determine the defective area of the product according to the correlation analysis result, and perform process improvement or equipment obstacle removal.

It should be noted that, the correlation analysis algorithm in the embodiment of the present disclosure may further include a plurality of correlation analysis algorithms, for example, the correlation analysis algorithm may include a Pearson correlation analysis method, a K-W test method and an M-W rank sum test method at the same time, and may also include other correlation analysis algorithms at the same time. It can be appreciated that the correlation analysis algorithm includes a plurality of correlation analysis algorithms, i.e., a plurality of correlation analysis results can be obtained, thereby providing more evidence for the staff.

Illustratively, when the correlation analysis algorithm includes both the Pearson correlation analysis method and the K-W test method, in combination with the example in step 201 described above, the parameter analysis device may output the correlation analysis result in step 204 in the manner of table 2 below.

Table 2 correlation analysis results table

Wherein Step represents the station number of the inspection station, and Item represents the type of inspection parameter.

As one possible embodiment of the present disclosure, in conjunction with fig. 2, as shown in fig. 7, the parameter analysis method provided by the present disclosure further includes the following steps:

in step 701, the parameter analysis device determines a contour map of a plurality of sets of second detection parameters.

The contour map is used for representing the magnitude of detection parameters corresponding to each region on the product. At present, the contour map is generally applied to the fields of geographic exploration and map drawing, namely, points with the same ground surface height are connected into a loop line which is directly projected onto a plane to form a horizontal curve, and the loop lines with different heights cannot be matched. The characteristic of the contour map, combined with the embodiment of the disclosure, can intuitively represent the magnitude conditions of the detection parameters of different areas of the product, so as to assist staff in analyzing the correlation analysis results of the detection parameters.

Step 702, the parameter analysis device outputs a contour map of a plurality of groups of second detection parameters.

Alternatively, the parameter analyzing means may output the contour map of the plurality of sets of the second detection parameters in a plurality of ways.

In one possible implementation, in conjunction with the example in step 202, two contour plots are shown in fig. 8 and 9, with fig. 8 representing the contour plot of the IOFF parameter in the Glass of the slice and fig. 9 representing the contour plot of the THK parameter in the Glass of the slice. It can be seen that the closer to the middle region of Glass, the smaller the IOFF parameter value, conversely, the larger the THK parameter. Thus, the staff can clearly see that there is a negative correlation between the IOFF parameter and the THK parameter.

The embodiment of the disclosure may divide the functional modules or functional units of the parameter analysis apparatus according to the above method example, for example, each functional module or functional unit may be divided corresponding to each function, or two or more functions may be integrated in one processing module. The integrated modules may be implemented in hardware, or in software functional modules or functional units. The division of modules or units in the embodiments of the present disclosure is merely a logic function division, and other division manners may be actually implemented.

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

Wherein the obtaining unit 1001 is configured to obtain a first detection parameter of the product. For example, in connection with fig. 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 an interpolation algorithm, and obtain a second detection parameter. For example, in connection with fig. 2, the processing unit 1002 is specifically configured to perform step 202.

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

An output unit 1003 configured to output a correlation evaluation value of the second detection parameter. For example, in connection with fig. 2, the output unit 1003 is specifically configured to perform step 204.

In some embodiments, the processing unit 1002 is further configured to determine a coordinate distance between the first measurement point and the second measurement point and the half variance. For example, in connection with fig. 5, the processing unit 1002 is specifically configured to perform step 501.

In some embodiments, the processing unit 1002 is further configured to determine a half variance of the plurality of predicted points based on the distance between the first measurement point and the second measurement point and the half variance. For example, in connection with fig. 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 coefficients from the half variances of the plurality of predicted points. For example, in connection with fig. 5, the processing unit 1002 is specifically configured to perform step 503.

In some embodiments, the processing unit 1002 is further configured to determine a target interpolation for the plurality of predicted points based on the weight coefficient and the plurality of sets of first detection parameters. For example, in connection with fig. 5, the processing unit 1002 is specifically configured to perform step 504.

In some embodiments, the processing unit 1002 is further configured to interpolate the plurality of sets of first detection parameters according to the target interpolation of the plurality of predicted points, and determine a plurality of sets of second detection parameters. For example, in connection with fig. 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 half variance fit curve from the coordinate distance between the first measurement point and the second measurement point and the half variance. For example, in connection with fig. 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 half variance of the plurality of predicted points according to a half variance fitting curve. For example, in connection with fig. 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, in connection with fig. 6, the processing unit 1002 is specifically configured to perform step 601.

In some embodiments, the processing unit 1002 is further configured to determine a rank of the ordered plurality of sets of second detection parameters. For example, in connection with fig. 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 the plurality of sets of second detection parameters according to a rank of the plurality of sets of second detection parameters. For example, in connection with fig. 6, the processing unit 1002 is specifically configured to perform step 603.

In some embodiments, the processing unit 1002 is further configured to determine a correlation evaluation value between the plurality of sets of second detection parameters based on statistics of the plurality of sets of second detection parameters. For example, in connection with fig. 6, the processing unit 1002 is specifically configured to perform step 604.

In some embodiments, the processing unit 1002 is further configured to determine a contour map of the plurality of sets of second detection parameters. For example, in connection with fig. 7, the processing unit 1002 is specifically configured to perform step 701.

In some embodiments, the output unit 1003 is further configured to output a contour map of the plurality of sets of second detection parameters. For example, in connection with fig. 6, the output unit 1003 is specifically configured to perform step 702.

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

In some embodiments, the obtaining unit 1001 is further configured to obtain a plurality of sets of first detection parameters of the product according to the statistical aggregation table. For example, in connection with fig. 2, the acquisition unit 1001 is specifically configured to perform step 201.

In some embodiments, the obtaining unit 1001 is further configured to obtain a third detection parameter of the plurality of products from the detection device according to the sea Du Pu database. For example, in connection with fig. 2, the acquisition unit 1001 is specifically configured to perform step 201.

In some embodiments, the processing unit 1002 is further configured to aggregate data of the third detection parameters of the plurality of products according to a structured query language SQL, and determine the HBase statistics aggregation table. For example, in connection with fig. 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 sets of second detection parameters before outputting the correlation evaluation values between the sets of second detection parameters. For example, in connection with fig. 2, the processing unit 1002 is specifically configured to perform step 204.

Alternatively, the parameter analysis device 1000 may further include a storage unit (shown in fig. 10 as a dashed box) in which a program or instructions are stored. When the processing unit 1002 executes the program or instructions, the parameter analysis apparatus 1000 is enabled to execute the parameter analysis method described in the above-described method embodiment.

In addition, the technical effects of the parameter analysis device shown in fig. 10 may refer to the technical effects of the parameter analysis method described in the above embodiment, and will not be described herein.

Fig. 11 shows a further 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, e.g., perform the steps performed by the acquisition unit 1001, the processing unit 1002, and the output unit 1003 described above, and/or be configured to perform other processes of the techniques described herein. The communication interface 1103 is configured to support communication of the parameter analysis device 1100 with other network entities. Parameter analysis apparatus 1100 may also include a memory 1101 and a bus 1104, memory 1101 being configured to store program codes and data for parameter analysis apparatus 1100.

Wherein the memory 1101 may be a memory or the like in the parameter analysis apparatus 1100, which may include a volatile memory such as a random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid state disk; the memory may also comprise a combination of the above types of memories.

The processor 1102 may be implemented or executed with various exemplary logic blocks, modules and circuits described in connection with this 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 device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various exemplary logical blocks, modules, and circuits described in connection with the present disclosure. The processor may also be a combination that performs the function of a computation, e.g., a combination comprising one or more microprocessors, a combination of a DSP and a microprocessor, etc.

Bus 1104 may be an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus or the like. The bus 1104 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 11, but not only one bus or one type of bus.

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

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

In some implementations, the memory 1101 stores elements, execution modules or data structures, or a subset thereof, or an extended set thereof.

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

From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.

Some embodiments of the present disclosure provide a computer readable storage medium (e.g., a non-transitory computer readable storage medium) having stored therein computer program instructions that, when run on a computer (e.g., a parameter analysis device), cause the computer to perform a parameter analysis method as described in any of the above embodiments.

Illustratively, the above computer-readable storage medium may include, but is not limited to: magnetic storage devices (e.g., hard Disk, floppy Disk or magnetic strips, etc.), optical disks (e.g., CD (Compact Disk), DVD (Digital Versatile Disk ), etc.), smart cards, and flash Memory devices (e.g., EPROM (Erasable Programmable Read-Only Memory), card, stick, key drive, etc.). 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" can include, without being limited to, wireless channels and various other media capable of storing, containing, and/or carrying instruction(s) and/or data.

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

Some embodiments of the present disclosure also provide a computer program. The computer program, when executed on a computer (e.g. a parameter analysis device), causes the computer to perform the parameter analysis method as described in the above embodiments.

The beneficial effects of the computer readable storage medium, the computer program product and the computer program are the same as those of the parameter analysis method described in some embodiments, and are not described herein.

In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, indirect coupling or communication connection of devices or units, electrical, mechanical, or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

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

The foregoing is merely a specific embodiment of the disclosure, but the protection scope of the disclosure is not limited thereto, and any person skilled in the art who is skilled in the art will recognize that changes or substitutions are within the technical scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

A method of analysis of a detection parameter, comprising:

obtaining a plurality of groups of first detection parameters of a product; the first detection parameters comprise detection parameters of a plurality of measurement points, and the measurement points are position points on the product;

Performing interpolation processing on the multiple groups of first detection parameters according to an interpolation algorithm 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;

determining correlation evaluation values among the plurality of groups of second detection parameters according to a correlation analysis algorithm; the correlation evaluation value is used for representing the correlation among the plurality of groups of second detection parameters corresponding to each measurement point;

and outputting the correlation evaluation values among the plurality of groups of second detection parameters.
The method of claim 1, wherein the interpolation algorithm is Kriging interpolation;

and performing interpolation processing on the multiple groups of first detection parameters according to an interpolation algorithm to determine multiple groups of second detection parameters, wherein the method comprises the following steps:

determining a coordinate distance between the first measurement point and the second measurement point and a half variance; the first measuring point and the second measuring point are measuring points in the plurality of measuring points corresponding to the same group of first detection parameters;

determining the half variance of a plurality of predicted points according to the distance between the first measuring point and the second measuring point and the half variance; the predicted points are measurement points which do not have corresponding detection parameters in each group of the first detection parameters in the plurality of measurement points corresponding to the groups of the first detection parameters;

Determining a weight coefficient according to the half variances of the plurality of predicted points;

determining target interpolation of the plurality of predicted points according to the weight coefficient and the plurality of groups of first detection parameters;

and carrying out interpolation processing on the plurality of groups of first detection parameters according to target interpolation of the plurality of predicted points, and determining the plurality of groups of second detection parameters.
The method of claim 2, wherein the determining the half variance of the plurality of predicted points based on the coordinate distance between the first measurement point and the second measurement point and the half variance comprises:

determining a semi-variance fitting curve according to the coordinate distance between the first measuring point and the second measuring point and the semi-variance;

and determining the half variances of the plurality of predicted points according to the half variance fitting curve.
A 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:

wherein d _ij Representing the coordinate distance between the first measurement point and the second measurement point, i representing the number of the first measurement point, x _i Representing the abscissa, y, of the first measurement point _i An ordinate representing the first measurement point, j representing the number of the second measurement point, x _j Representing the abscissa, y, of the second measurement point _j Representing the ordinate of the second measurement point;

the half variance between the first measurement point and the second measurement point satisfies the following formula:

wherein r is _ij Representing the half variance between the first measurement point and the second measurement point, E representing the covariance, z _i A detection parameter, z, representing the first measurement point _j A detection parameter representing the second measurement point;

the target interpolation of the plurality of predicted points satisfies the following formula:

wherein,target interpolation, lambda representing the plurality of predicted points _k Representing the weight coefficient, z _k Representing a detection parameter of said measuring point numbered k.
The method of claim 1, wherein the correlation analysis algorithm is Pearson correlation analysis, 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, Y represents one of the second detection parameters, μ, respectively _X Mean town, mu, of the second detection parameter X _Y Represents the average value, sigma, of the second detection parameter Y _X Represents the standard deviation, sigma, of the second detection parameter X _Y Representing the standard deviation of the second detection parameter Y.
The method of claim 1, wherein the correlation analysis algorithm is a Kruskal-Wallis test method;

the determining the correlation evaluation value among the plurality of sets of second detection parameters according to the correlation analysis algorithm comprises the following steps:

sorting the plurality of groups of second detection parameters according to the increasing order;

determining the ranks of the ordered multiple groups of second detection parameters;

determining statistics of the plurality of groups of second detection parameters according to the ranks of the plurality of groups of second detection parameters;

and determining correlation evaluation values among the plurality of groups of second detection parameters according to the statistics of the plurality of groups of second detection parameters.
The method of 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 plurality of sets of second detection parameters, N represents the number of detection parameters included in the plurality of sets of second detection parameters, N represents the number of detection parameters included in one of the second detection parameters, R _X Representing the sum of the ranks of the second detection parameters X, R _Y Representing the sum of the ranks of the second detection parameters Y;

the correlation evaluation values among the plurality of sets of second detection parameters satisfy the following formula:

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

determining a contour map of the plurality of sets of second detection parameters; the contour map is used for representing the magnitude of detection parameters corresponding to each region on the product;

and outputting the contour maps of the plurality of groups of second detection parameters.
The method of any of claims 1-8, wherein the obtaining a plurality of sets of first detection parameters for a product comprises:

determining a statistical aggregation table;

and obtaining the multiple groups of first detection parameters of the product according to the statistical aggregation table.
The method of claim 9, wherein the statistical aggregation table is a sea Du Pu database HBase statistical aggregation table, and the determining the HBase statistical aggregation table comprises:

acquiring third detection parameters of a plurality of products from detection equipment according to the sea Du Pu database; the third detection parameter includes the first detection parameter;

and carrying out data aggregation on the third detection parameters of the plurality of products according to a structured query language SQL, and determining the HBase statistical aggregation table.
The method of any of claims 1-10, wherein the plurality of sets of first detection parameters includes critical process parameters and electro-permanent magnet EPM electrical parameters; the key technological parameters comprise at least one of surface resistance RS parameters, involution precision TP parameters, line width CD parameters, film thickness THK parameters and register precision OL parameters; the electrical parameters include at least one of a threshold voltage VTH parameter, a mobility MOB parameter, an operating current ION parameter, and a reverse off-current IOFF parameter.
The method of any of claims 1-11, further comprising:

and before outputting the correlation evaluation values among the plurality of groups of second detection parameters, sorting the correlation evaluation values among the plurality of groups of second detection parameters.
A detection parameter analysis device, comprising: the device comprises an acquisition unit, a processing unit and an output unit;

the acquisition unit is configured to acquire a plurality of groups of first detection parameters of the product; the first detection parameters comprise detection parameters of a plurality of measurement points, and the measurement points are position points on the product;

the processing unit is configured to perform interpolation processing on the multiple groups of first detection parameters according to an interpolation algorithm, and determine multiple groups of second detection parameters; the number of the second detection parameters is the same as the number of the first detection parameters;

The processing unit is further configured to determine correlation evaluation values among the plurality of groups of second detection parameters according to a correlation analysis algorithm; the correlation evaluation value is used for representing the correlation among the plurality of groups of second detection parameters corresponding to each measurement point;

the output unit is configured to output a correlation evaluation value of the second detection parameter.
A detection parameter analysis device comprises a processor and a communication interface; the communication interface being coupled to the processor for executing a computer program or instructions to implement the method of analyzing a detection parameter according to any of claims 1-12.
A detection parameter analysis system comprising detection parameter analysis means for performing the detection parameter analysis method of any one of the preceding claims 1-12.
A test parameter analysis application, wherein the test parameter analysis application comprises an application interaction interface which, when subjected to a preset operation, causes the test parameter analysis application to perform the test parameter analysis method of any one of the preceding claims 1-12.
A non-transitory computer readable storage medium having instructions stored therein that, when executed by a computer, perform the method of analyzing a detection parameter of any of the preceding claims 1-12.
A computer program product comprising computer program instructions which, when executed on a computer, cause the computer to perform the method of analysis of a detected parameter according to any one of claims 1 to 12.