A preparation method for a high-precision ultra-thin glass substrate

Abstract

The present application provides a method for preparing a high-precision ultra-thin glass substrate, which relates to the field of glass preparation and includes: obtaining the surface feature vectors of each sampling region of the glass substrate to be processed; if the number of sampling regions with classification labels as key labels is greater than a preset threshold, obtaining the preset classification vector with the highest matching degree with the surface feature vector of each sampling region among a number of preset classification vectors as the target classification vector corresponding to the sampling region; obtaining the normalized weight of each sampling region according to the influence fluctuation value of the target classification vector corresponding to each sampling region; and obtaining the fusion feature vector of the glass substrate to be processed according to the surface feature vector and the normalized weight of each sampling region. The target pressure curve obtained in the present application is more suitable for the glass substrate to be processed than the target pressure curve obtained from the fusion feature vector directly fused without setting weights, so that the processing effect of the glass substrate to be processed is better.

Description

Background Art

[0002] In the glass manufacturing industry, glass processing, as a key process to improve the performance and quality of glass, is of great importance. Different processing steps such as fine grinding, rough polishing, and fine polishing play a decisive role in the final flatness, smoothness, and strength of the glass, and the precise control of pressure is the core point among them.

[0003] Currently, in the technical applications of glass processing, a fixed pressure value is usually set for each processing step. Although this method takes into account the differences in different steps, there are significant defects in the setting of the fixed pressure value within a single processing step. In the same processing step, as the processing time progresses, the processing tool will gradually wear, and its contact state and cutting ability with the glass will continuously change. If a fixed pressure value is always used, in the initial stage of tool wear, its cutting effect may not be fully exerted, resulting in low processing efficiency; while in the later stage, due to severe tool wear, the same fixed pressure may cause insufficient processing force and fail to achieve the expected processing effect. In addition, during the glass processing, the material removal situation and microstructure on its surface continuously change, and a single fixed pressure value cannot flexibly adapt to these changes. For example, in the fine grinding step, the glass surface gradually becomes flat from the initial rough state. If the pressure remains fixed, it may cause over-processing of the already flat surface in the later stage, damaging the surface quality and affecting the optical performance and mechanical strength of the glass. Therefore, there is an urgent need to develop a more advanced pressure control technology to optimize the glass processing process and meet the growing demand for high-quality glass production. Summary of the Invention

[0004] In view of the above technical problems, the present application provides a method for preparing a high-precision ultra-thin glass substrate, which at least partially solves the problems existing in the prior art.

[0005] In a first aspect of the present application, there is provided a method for preparing a high-precision ultra-thin glass substrate, the method comprising:

[0006] Obtaining a surface feature vector of each sampling area of the glass substrate to be processed; wherein each sampling area has a classification label; the classification label characterizes the influence degree of the pressure curve category on the performance parameters of the sampling area; the classification label includes a key label and a non-key label; the glass substrate to be processed is the glass substrate before the light pressure stage processing;

[0007] If the number of sampling regions with classification labels as key labels is greater than a preset threshold, obtain, from a number of preset classification vectors, the preset classification vector with the highest matching degree with the surface feature vector of each sampling region as the target classification vector corresponding to the sampling region; the glass samples corresponding to each preset classification vector are all qualified glass samples; the glass samples corresponding to each preset classification vector have an influence fluctuation value; the influence fluctuation values corresponding to any two preset classification vectors are different; the influence fluctuation value characterizes the degree to which the performance parameters of the glass sample are affected by the preset pressure curve category;

[0008] Obtain the normalized weight of each sampling region according to the influence fluctuation value of the target classification vector corresponding to each sampling region; wherein, the influence fluctuation value is proportional to the normalized weight;

[0009] Obtain the fusion feature vector of the glass substrate to be processed according to the surface feature vector and the normalized weight of each sampling region; wherein, the fusion feature vector is used to determine the target pressure curve list with the highest matching degree in the pressure curve lists corresponding to a number of historical qualified glass samples for the glass substrate to be processed, and prepare the glass substrate to be processed based on the target pressure curve list.

[0010] This application has at least the following beneficial effects:

[0011] The method for preparing a high-precision ultra-thin glass substrate provided by this application first performs a rough classification of key tags / non-key tags on the surface feature vectors corresponding to each sampling area to determine whether different weights need to be assigned to each sampling area for feature fusion later; secondly, when the number of sampling areas with a classification tag of key tag is greater than a preset threshold, that is, when most sampling areas in the glass substrate to be processed are sensitive to the pressure curve, several preset classification vectors are used to assign the most suitable influence fluctuation value to each sampling area. This is because, through the rough classification of the classification tags in the first step, only a preliminary understanding can be obtained of whether each sampling area is sensitive to the pressure curve category, and further processing can obtain the sensitivity degree of each sampling area to different pressure curves. Furthermore, the influence fluctuation value of the target classification vector corresponding to each sampling area is used as the weight of each sampling area, and then normalization is performed so that the sum of the normalized weights of all sampling areas corresponding to the glass substrate to be processed is 1. Finally, the surface feature vectors corresponding to each sampling area of the glass substrate to be processed are fused to obtain the fused feature vector of the glass substrate to be processed. This application determines the weight for each sampling area, that is, assigns the corresponding weight to the surface feature vector corresponding to each sampling area during feature fusion and then performs the fusion. In this way, the obtained fused feature vector can more comprehensively and objectively highlight the sensitivity degree to different pressure curves. Furthermore, according to the obtained fused feature vector, the target pressure curve with the highest matching degree with the glass substrate to be processed is determined in the pressure curve list corresponding to several historical qualified glass samples. The target pressure curve obtained by this application is more suitable for the glass substrate to be processed than the target pressure curve obtained by directly fusing the fused feature vector without setting weights, so as to make the processing effect of the glass substrate to be processed better. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 It is a flowchart of the method for preparing a high-precision ultra-thin glass substrate provided by the embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0015] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of this application are used to distinguish similar objects and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of this application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0016] It should be noted that the following describes various aspects of embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement a device and / or practice a method. In addition, this device can be implemented and this method can be practiced using other structures and / or functions in addition to one or more of the aspects described herein.

[0017] Please refer to Figure 1 As shown, an embodiment of this application provides a method for preparing a high-precision ultra-thin glass substrate, and the method includes:

[0018] S100, obtaining a surface feature vector of each sampling area of the glass substrate to be processed; wherein, each sampling area has a classification label; the classification label characterizes the influence degree of the pressure curve category on the performance parameters of the sampling area; the classification label includes a key label and a non-key label; the glass substrate to be processed is the glass substrate before the light pressing stage.

[0019] Specifically, the glass substrate to be processed may have several sampling areas. Among them, infrared images of each sampling area of the glass substrate to be processed are respectively obtained, and a CNN is used to extract features from the infrared images of each sampling area, so as to obtain the surface feature vector corresponding to each sampling area. And the glass substrate to be processed is the glass substrate before the light pressing stage. Here, in the process of preparing a high-precision ultra-thin glass substrate, starting from the light pressing stage, pressure needs to be applied in the fine grinding link, rough polishing link, and fine polishing link of the light pressing stage; the fine grinding link, rough polishing link, and fine polishing link of the heavy pressing stage; and the fine grinding link, rough polishing link, and fine polishing link of the trimming stage.

[0020] As an example: If the glass substrate to be processed is a square glass substrate, its sampling areas can be the areas corresponding to the four corners and the area corresponding to the geometric center. The total area of the sampling areas is less than the area of the entire glass substrate to be processed. When selecting the sampling areas, they should be evenly distributed at different positions on the glass substrate, and they should be able to represent the comprehensive surface feature conditions of the entire glass substrate.

[0021] In addition, each sampling area has a corresponding classification label. Here, the classification labels include key labels and non-key labels. Among them, the classification labels can be obtained through a classification model. The classification labels represent the degree of influence of the pressure curve category during processing on the performance parameters of the sampling area. Further, the key label indicates that the pressure curve category during processing has a greater degree of influence on the performance parameters of the sampling area; conversely, the non-key label indicates that the pressure curve category during processing has a smaller degree of influence on the performance parameters of the sampling area. That is, the key label indicates that when processing this sampling area, using different pressure curves, the numerical values of the performance parameters finally obtained for this sampling area are not very different; conversely, the non-key label indicates that when processing this sampling area, using different pressure curves, the numerical values of the performance parameters finally obtained for this sampling area are quite different.

[0022] The pressure curve can be several pre-set pressure curves, which are used to set the grinding pressure according to the pressure curve in a grinding process, rather than using a constant pressure. This is because as the grinding process progresses, the surface state of the glass continuously changes. Using the pressure curve can accurately adjust the pressure magnitude according to the actual requirements at different stages. Compared with a constant pressure value, the performance of the glass processed using the pressure curve is better.

[0023] It should be noted that the grinding process in this embodiment can include: fine grinding, rough polishing, and fine polishing in the light pressure stage; fine grinding, rough polishing, and fine polishing in the heavy pressure stage; fine grinding, rough polishing, and fine polishing in the trimming stage, etc.

[0024] In this embodiment, a rough classification of key label / non-key label is performed on the surface feature vector corresponding to each sampling area to determine whether different weights need to be assigned to each sampling area for feature fusion later.

[0025] S200. If the number of sampling regions with classification labels as key labels is greater than a preset threshold, obtain, among a number of preset classification vectors, the preset classification vector with the highest matching degree with the surface feature vector of each sampling region as the target classification vector corresponding to the sampling region; the glass samples corresponding to each preset classification vector are all qualified glass samples; the glass samples corresponding to each preset classification vector have influence fluctuation values; the influence fluctuation values of any two preset classification vectors are different; the influence fluctuation value represents the degree to which the performance parameters of the glass sample are affected by the preset pressure curve category.

[0026] Specifically, if the number of sampling regions with classification labels as key labels is greater than a preset threshold, it indicates that most of the sampling regions in the glass substrate to be processed are sensitive to the pressure curve. Such sampling regions have a greater impact on the performance parameters of the glass after being processed with different pressure curves, while there may still be some sampling regions that are not sensitive to the pressure curve, that is, the performance parameters of the glass obtained after processing such sampling regions with different pressure curves are basically the same. At this time, it indicates that most regions of the glass sample to be tested are sensitive to the pressure curve, while there may still be some sampling regions that are not sensitive to the pressure curve. At this time, a suitable target classification vector is matched for each sampling region. The target classification vector is one of the preset classification vectors. Here, each preset classification vector has a corresponding qualified glass sample. A qualified glass sample means that each performance parameter of the glass obtained after processing is qualified. And the preset classification vector is the surface feature vector obtained by using CNN to extract features from the infrared image of the corresponding qualified glass sample. It should be noted that in this embodiment, the size of the qualified glass sample can be the same as the size of a sampling region of the glass substrate to be processed, so as to obtain a more accurate matching result.

[0027] In addition, the glass samples corresponding to each preset classification vector have influence fluctuation values. Here, the influence fluctuation value represents the degree to which the performance parameters of the glass sample are affected by the preset pressure curve category. Here, as above, the greater the influence fluctuation value, the greater the fluctuation of the performance parameters of the glass obtained after processing the qualified glass sample with different pressure curves. On the contrary, the smaller the influence fluctuation value, the smaller the fluctuation of the performance parameters of the glass obtained after processing the qualified glass sample with different pressure curves.

[0028] In this embodiment, when the number of sampling regions with classification labels as key labels is greater than a preset threshold, that is, when most of the sampling regions in the glass substrate to be processed are sensitive to the pressure curve, several preset classification vectors are used to assign the most suitable influence fluctuation value to each sampling region. This is because, after the rough classification of the classification labels in the first step, it is only possible to initially know whether each sampling region is sensitive to the pressure curve category, and the further processing in this embodiment can obtain the sensitivity degree of each sampling region to different pressure curves.

[0029] It should be noted that when matching the surface feature vector of each sampling region with each preset classification vector, the matching degree can be obtained by using the Euclidean distance, cosine distance, etc.

[0030] S300. Obtain the normalized weight of each sampling region according to the influence fluctuation value of the target classification vector corresponding to each sampling region; wherein, the influence fluctuation value is proportional to the normalized weight.

[0031] Specifically, the influence fluctuation value of the target classification vector corresponding to each sampling region is used as the weight of each sampling region, and then normalization is performed so that the sum of the normalized weights of all sampling regions corresponding to the glass substrate to be processed is 1.

[0032] S400. Obtain the fusion feature vector of the glass substrate to be processed according to the surface feature vector and the normalized weight of each sampling region; wherein, the fusion feature vector is used to determine the target pressure curve list with the highest matching degree in the pressure curve lists corresponding to several historical qualified glass samples for the glass substrate to be processed, and the glass substrate to be processed is prepared based on the target pressure curve list.

[0033] Specifically, by fusing the surface feature vectors corresponding to each sampling region of the glass substrate to be processed, the fusion feature vector of the glass substrate to be processed can be obtained. In this embodiment, a weight is determined for each sampling region, that is, a corresponding weight is assigned to the surface feature vector corresponding to each sampling region during feature fusion and then fused. In this way, the obtained fusion feature vector can more comprehensively and objectively highlight the sensitivity degree to different pressure curves. Furthermore, according to the obtained fusion feature vector, the target pressure curve with the highest matching degree with the glass substrate to be processed is determined in the pressure curve lists corresponding to several historical qualified glass samples. The target pressure curve obtained in this embodiment is more suitable for the glass substrate to be processed than the target pressure curve obtained by directly fusing the fusion feature vector without setting weights. Finally, the glass substrate to be processed is prepared based on the target pressure curve list.

[0034] In an exemplary embodiment of the present application, after obtaining the surface feature vector corresponding to each sampling region of the glass substrate to be processed, the method further includes:

[0035] For S500, if the number of sampling regions with classification labels as key labels is equal to or less than a preset threshold, a fused feature vector of the glass substrate to be processed is obtained according to the surface feature vector corresponding to each sampling region.

[0036] Specifically, if the number of sampling regions with classification labels as key labels is equal to or less than a preset threshold, it indicates that most of the sampling regions in the glass substrate to be processed are less sensitive to the pressure curve. Such sampling regions have little impact on the performance parameters of the glass after being processed with different pressure curves. Therefore, in this embodiment, the fused feature vector is directly obtained. Then, the target pressure curve is determined for the glass substrate to be tested according to the fused feature vector.

[0037] In an exemplary embodiment of the present application, the fused feature vector determines the target pressure curve list with the highest matching degree in the pressure curve lists corresponding to several historical qualified glass samples for the glass substrate to be processed according to the following steps:

[0038] S410, obtain the historical surface feature vector corresponding to the historical qualified glass sample before the light pressing stage to obtain the historical surface feature vector list LT = (LT1, LT2,..., LT i ,..., LT n ); i = 1, 2,..., n; where n is the number of historical qualified glass samples; LT i is the historical surface feature vector corresponding to the i-th historical qualified glass sample before the light pressing stage; each historical qualified glass sample has a corresponding historical pressure curve list; the historical pressure curve list includes each pressure curve corresponding to each process link that requires pressure starting from the light pressing stage.

[0039] Specifically, the historical qualified glass sample can be the same size as the glass substrate to be processed and have the same division of sampling regions. The historical surface feature vector is obtained by performing feature extraction on the infrared image of each sampling region corresponding to the corresponding historical qualified glass sample before the light pressing stage using a CNN and then performing feature fusion. Each historical qualified glass sample has a corresponding historical pressure curve list. Here, each historical qualified glass sample may have several corresponding pressure curve lists such that its finally obtained performance parameters are qualified. In this embodiment, the historical pressure curve list corresponding to each historical qualified glass sample can be the one with the best corresponding performance parameters among all the pressure curve lists corresponding to the historical qualified glass sample that make its finally obtained performance parameters qualified. The historical pressure curve list includes each pressure curve corresponding to each process link that requires pressure starting from the light pressing stage. That is, each process link that requires pressure has a corresponding pressure curve.

[0040] It should be noted that here, the performance parameters may include substrate parallelism, flatness, surface finish, and roughness. Among them, the closer the substrate parallelism is to zero, the more uniform and stable the light refraction and propagation of the glass in optical applications. For example, in precision optical instruments, a substrate with high parallelism can ensure the imaging quality and reduce aberration. If the parallelism deviation is large, the light propagation will deviate, affecting the use effect of the glass in the optical field. The closer the flatness is to zero, the flatter the surface. In fields with extremely high requirements for flatness such as semiconductor manufacturing, a glass substrate with high flatness can ensure the accuracy of processes such as lithography and improve the yield rate of chip manufacturing. If the flatness is poor, problems such as uneven coating and deformed lithography patterns will occur. Generally, the higher the surface finish, the better. A high surface finish means that the glass surface is smooth and flawless, which can reduce light scattering and improve the light transmittance and reflection performance of the glass. In applications such as optical lenses and displays, glass with high surface finish can present clearer images and better visual effects. Generally, the smaller the roughness, the better. Roughness is the opposite of surface finish. A small roughness indicates that the microscopic undulations on the glass surface are small, which can improve the mechanical strength of the glass, reduce the risk of cracking caused by stress concentration due to surface defects, and also avoid problems such as light diffuse reflection caused by a rough surface in terms of optical performance.

[0041] It can be understood that for the glass substrate to be processed, the judgment criteria for the quality of its performance parameters can be adjusted according to its corresponding use. However, the numerical values of each performance parameter can be used for quantitative judgment.

[0042] S420, cluster the LTs to obtain a list of vector clustering clusters YJ = (YJ1, YJ2,..., YJ p ,..., YJ q ); p = 1, 2,..., q; where q is the number of vector clustering clusters; YJ p is the p-th vector clustering cluster; the matching degree between any two historical surface feature vectors within the same vector clustering cluster is greater than a preset matching degree threshold; YJ p = (YJ p，1 , YJ p，2 ,..., YJ p，a ,..., YJ p，f（p） ); a = 1, 2,..., f(p); f(p) is the number of historical surface feature vectors included in the p-th vector clustering cluster; YJ p，a is the a-th historical surface feature vector included in the p-th vector clustering cluster; each vector clustering cluster has a corresponding flag vector; the flag vector is the central vector of the corresponding vector clustering cluster; each vector clustering cluster has a corresponding list of key historical pressure curves; the list of key historical pressure curves is the list of historical pressure curves corresponding to the historical qualified glass samples within the corresponding vector clustering cluster with the smallest comprehensive difference from the standard performance parameters.

[0043] Specifically, clustering is performed based on the historical surface feature vectors corresponding to each historical qualified glass sample among a number of historical qualified glass samples. Each class within a vector clustering cluster is a class of historical qualified glass samples, that is, the surface features of the historical qualified glass samples within the same vector clustering cluster are similar.

[0044] The central vector can be the average vector of all historical surface feature vectors within the corresponding vector clustering cluster, or can be the historical surface feature vector with the highest matching degree to the corresponding average vector within the vector clustering cluster. The list of key historical pressure curves is the list of historical pressure curves corresponding to the historical qualified glass sample with the smallest comprehensive difference from the standard performance parameters within the corresponding vector clustering cluster. The standard performance parameters are the normalized sum of the standard values of each performance parameter corresponding to the substrate to be tested. The comprehensive difference refers to the total difference obtained after normalizing the differences between the values of all performance parameters and their corresponding standard values. Here, the purpose of the normalization process is to unify the dimensions between the numerical values of each index, so that the obtained comprehensive difference is more referenceable. That is, the list of key historical pressure curves is the list of historical pressure curves corresponding to the historical qualified glass sample with the best comprehensive performance parameters within the corresponding vector clustering cluster.

[0045] S430. According to DT and YJ, obtain the first matching degree list YP = (YP1, YP2,..., YP p ,..., YP q ); where YP p is the matching degree between the flag vector BYT p of the p-th vector clustering cluster and DT; DT is the fusion feature vector of the glass substrate to be processed.

[0046] Specifically, YP p =(BYT p ·DT) / (|BYT p |×|DT|).

[0047] S440. Determine MAX(YP) as the target vector clustering cluster.

[0048] S450. Determine the list of key historical pressure curves corresponding to the target vector clustering cluster as the target pressure curve list.

[0049] In this embodiment, in order to reduce the data processing volume, first, the historical qualified glass samples with similar surface features are clustered through clustering. Furthermore, each vector clustering cluster has a corresponding flag vector for matching with the glass substrate to be processed, and each vector clustering cluster also has a list of historical pressure curves corresponding to the historical qualified glass sample with the best comprehensive performance parameters as the representative curve list of the clustering cluster. Thus, it avoids the surface feature matching between the glass substrate to be processed and each historical qualified glass sample, reduces the data processing volume, and saves the data processing time.

[0050] In an exemplary embodiment of the present application, after determining MAX(YP) as the target vector clustering cluster, the method further includes:

[0051] S460, obtaining the matching degree between DT and each historical surface feature vector in the target vector clustering cluster; to obtain a second matching degree list.

[0052] S470, determining the historical surface feature vector corresponding to the maximum matching degree in the second matching degree list as the target historical surface feature vector.

[0053] S480, determining the historical pressure curve list corresponding to the target historical surface feature vector as the target pressure curve list.

[0054] Specifically, in this embodiment, in order to reduce the amount of data processing, first, the historical qualified glass samples with similar surface features are clustered through clustering. Furthermore, each vector clustering cluster has a corresponding flag vector for matching with the glass substrate to be processed. After determining the target vector clustering cluster, it is further matched with each historical surface feature vector in the target vector clustering cluster. Finally, the historical surface feature vector corresponding to the maximum matching degree in the second matching degree list is determined as the target historical surface feature vector. The target historical surface feature vector determined in this way is more suitable for the surface vector of the glass substrate to be processed. And in this embodiment, through two matches and one clustering, full-scale matching of all historical surface feature vectors is avoided, the amount of data processing is reduced, and the data processing speed is improved.

[0055] In an exemplary embodiment of the present application, the influence fluctuation value of the glass sample corresponding to the preset classification vector is determined according to the following steps:

[0056] S210, obtaining the set HL=(HL1, HL2,..., HL x ,..., HL y ) of qualified pressure curve lists corresponding to the preset classification vector YF; x = 1, 2,..., y; where y is the number of qualified pressure curve lists corresponding to YF; HL x is the curve identifier of the x-th qualified pressure curve list corresponding to YF; the qualified pressure curve list includes each reasonable pressure curve corresponding to the process link that requires pressure starting from the light pressure stage; the reasonable pressure curve is the pressure curve that makes the corresponding glass sample a qualified glass sample after being processed;

[0057] S220, obtaining the influence fluctuation value YFB corresponding to YF according to HL and each performance parameter; where YFB meets the following conditions:

[0058] ;

[0059] where m is the number of performance parameters; ZB j is the index influence fluctuation value corresponding to the j-th performance parameter; j = 1, 2, …, m; ; C j is the list of index values corresponding to the j performance parameters obtained after processing the qualified glass samples corresponding to YF using each qualified pressure curve list in HL; C j = (C j，1 , C j，2 , …, C j，x , …, C j，y ); C j,x is the index value of the j performance parameters obtained after processing YF using HL x ; avg() is a preset average value determination function.

[0060] In this embodiment, the preset classification vector YF has corresponding qualified pressure curve lists. Although each qualified pressure curve list can make the corresponding glass samples qualified, the performance parameters of the glass samples finally obtained after being processed by different qualified pressure curve lists are some just at the passing line and some are relatively excellent. Obtain the influence fluctuation value of each performance parameter corresponding to YF. Here, if ZB j is larger, it means that the j-th performance parameter is more sensitive to the changes of different pressure curve lists; conversely, if ZB j is smaller, it means that the j-th performance parameter is less sensitive to the changes of different pressure curve lists, that is, the difference between the values of the j-th performance parameter for different pressure curves is smaller. Thus, the sensitivity degree of each performance parameter to the changes of different pressure curve lists is obtained, and finally the influence fluctuation value YFB corresponding to YF is obtained, that is, the influence fluctuation values of each performance parameter are integrated. Here, the influence fluctuation value is determined according to the corresponding variance formula and can reflect the fluctuation situation of the values of the performance parameters obtained after being processed according to different curve lists.

[0061] The influence fluctuation value determined in this embodiment can reflect the sensitivity degree of the corresponding sampling area to different pressure curve lists. It improves the accuracy of subsequent matching and the accuracy of the finally determined target pressure curve list.

[0062] In an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0063] Those skilled in the art can understand that various aspects of the present application can be implemented as a system, a method, or a program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuits", "modules", or "systems" here.

[0064] An electronic device according to this embodiment of the present application. The electronic device is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0065] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage mentioned above, and a bus connecting different system components (including the storage and the processor).

[0066] Among them, the storage stores program code, and the program code can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present application described in the "Exemplary Method" section of this specification.

[0067] The storage may include a readable medium in the form of a volatile storage, such as a random access storage (RAM) and / or a cache storage, and may further include a read-only storage (ROM).

[0068] The storage may further include a program / utility having a set (at least one) of program modules, and such program modules include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples.

[0069] The bus may represent one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any bus structure in a variety of bus structures.

[0070] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device (such as a router, a modem, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication can be carried out through an input / output (I / O) interface. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device through a bus. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0071] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0072] In an exemplary embodiment of the present application, there is also provided a computer-readable storage medium, on which there is a program product capable of implementing the above method of this specification. In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0073] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0074] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0075] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0076] The program code for performing the operations of the present application may be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0077] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes may be executed synchronously or asynchronously in, for example, multiple modules.

[0078] It should be noted that although several modules or units of a device for performing actions are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more of the above-mentioned modules or units may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided and embodied by multiple modules or units.

[0079] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for preparing a high-precision ultra-thin glass substrate, characterized in that, Including: Obtain the surface feature vector of each sampling area of the glass substrate to be processed; wherein, each sampling area has a classification label; the classification label characterizes the influence degree of the pressure curve category on the performance parameters of the sampling area; the classification label includes a key label and a non-key label; the glass substrate to be processed is the glass substrate before the light pressing stage processing; If the number of sampling areas with the classification label being the key label is greater than a preset threshold, then obtain the preset classification vector with the highest matching degree with the surface feature vector of each sampling area among several preset classification vectors as the target classification vector corresponding to the sampling area; the glass samples corresponding to each preset classification vector are all qualified glass samples; the glass samples corresponding to each preset classification vector have an influence fluctuation value; the influence fluctuation values corresponding to any two preset classification vectors are different; the influence fluctuation value characterizes the influence degree of the performance parameters of the glass sample by the preset pressure curve category; According to the influence fluctuation value of the target classification vector corresponding to each sampling area, obtain the normalized weight of each sampling area; wherein, the influence fluctuation value is proportional to the normalized weight; According to the surface feature vector and the normalized weight of each sampling area, obtain the fusion feature vector of the glass substrate to be processed; wherein, the fusion feature vector is used to determine the target pressure curve list with the highest matching degree for the glass substrate to be processed in the pressure curve lists corresponding to several historical qualified glass samples, and prepare the glass substrate to be processed based on the target pressure curve list.

2. The method for preparing a high-precision ultra-thin glass substrate according to claim 1, wherein, After obtaining the surface feature vector corresponding to each sampling area of the glass substrate to be processed, the method further includes: If the number of sampling areas with the classification label being the key label is equal to or less than the preset threshold, then obtain the fusion feature vector of the glass substrate to be processed according to the surface feature vector corresponding to each sampling area.

3. The method for preparing a high-precision ultra-thin glass substrate according to claim 1, wherein The fusion feature vector determines the target pressure curve list with the highest matching degree for the glass substrate to be processed in the pressure curve lists corresponding to several historical qualified glass samples according to the following steps: Obtain the historical surface feature vectors corresponding to the historical qualified glass samples before the light pressing stage to obtain a list of historical surface feature vectors LT=(LT1, LT2, …, LT i , …, LT n ); i = 1, 2, …, n; where n is the number of historical qualified glass samples; LT i is the historical surface feature vector corresponding to the i-th historical qualified glass sample before the light pressing stage; each historical qualified glass sample has a corresponding list of historical pressure curves; the list of historical pressure curves includes each pressure curve corresponding to each process step that requires pressure starting from the light pressing stage; Cluster the LTs to obtain a list of vector clusters YJ = (YJ1, YJ2, …, YJ p , …, YJ q ); p = 1, 2, …, q; where q is the number of vector clusters; YJ p is the p-th vector cluster; the matching degree between any two historical surface feature vectors within the same vector cluster is greater than a preset matching degree threshold; YJ p = (YJ p，1 , YJ p，2 , …, YJ p，a , …, YJ p，f（p） ); a = 1, 2, …, f(p); f(p) is the number of historical surface feature vectors included in the p-th vector cluster; YJ p，a is the a-th historical surface feature vector included in the p-th vector cluster; each vector cluster has a corresponding flag vector; the flag vector is the central vector of the corresponding vector cluster; each vector cluster has a corresponding list of key historical pressure curves; the list of key historical pressure curves is the list of historical pressure curves corresponding to the historical qualified glass samples with the smallest comprehensive difference from the standard performance parameters within the corresponding vector cluster; According to DT and YJ, the first matching degree list YP = (YP1, YP2,..., YP p ,..., YP q ) is obtained; where YP p is the matching degree between the flag vector BYT p of the p-th vector clustering cluster and DT; DT is the fusion feature vector of the glass substrate to be processed; Determine MAX(YP) as the target vector clustering cluster; Determine the key historical pressure curve list corresponding to the target vector clustering cluster as the target pressure curve list.

4. The method for preparing a high-precision ultra-thin glass substrate according to claim 3, characterized in that, YP p Meet the following characteristics: YP p =(BYT p ·DT) / (|BYT p |×|DT|)。 5. The method for preparing a high-precision ultra-thin glass substrate according to claim 3, wherein After determining MAX(YP) as the target vector clustering cluster, the method further includes: Obtain the matching degree between DT and each historical surface feature vector in the target vector clustering cluster; to obtain the second matching degree list; Determine the historical surface feature vector corresponding to the maximum matching degree in the second matching degree list as the target historical surface feature vector; Determine the historical pressure curve list corresponding to the target historical surface feature vector as the target pressure curve list.

6. The method for preparing a high-precision ultra-thin glass substrate according to claim 1, characterized in that, The influence fluctuation value of the glass sample corresponding to the preset classification vector is determined according to the following steps: Obtain a set of qualified pressure curve lists HL = (HL1, HL2,..., HL x ,..., HL y ) corresponding to the preset classification vector YF; x = 1, 2,..., y; where y is the number of qualified pressure curve lists corresponding to YF; HL x is the curve identifier of the x-th qualified pressure curve list corresponding to YF; the qualified pressure curve list includes each reasonable pressure curve corresponding to the process link that requires pressure starting from the light pressure stage; the reasonable pressure curve is the pressure curve that makes the corresponding glass sample a qualified glass sample after being processed; According to HL and each performance parameter, obtain the influence fluctuation value YFB corresponding to YF.

7. The method for preparing a high-precision ultra-thin glass substrate according to claim 6, wherein, YFB meets the following conditions: ； where m is the number of performance parameters; ZB j is the index influence fluctuation value corresponding to the j-th performance parameter; j = 1, 2, …, m; ; C j is the list of index values corresponding to the j performance parameters obtained after processing the qualified glass samples corresponding to YF using each qualified pressure curve list in HL; C j = (C j，1 , C j，2 , …, C j，x , …, C j，y ); C j,x is the index value of the j performance parameters obtained after processing YF using HL x ; avg() is a preset average value determination function.

8. The method for preparing a high-precision ultra-thin glass substrate according to claim 6, wherein The performance parameters include substrate parallelism, flatness, surface finish and roughness.

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