Method and device for determining wafer surface friction coefficient in semiconductor manufacturing process

Abstract

The invention provides a wafer surface friction coefficient determination method and device applied to a semiconductor manufacturing process, and the method comprises the steps: carrying out the shooting under the irradiation of light sources of various colors, obtaining the color images of various colors, carrying out the processing, and completing the denoising of a gray-scale image corresponding to the color image of each color, thereby obtaining the surface friction coefficient of a wafer. The grey-scale maps can well show the characteristics of the wafer surface, and in order to avoid the influence of a single-color light source on the result, the grey-scale maps corresponding to multiple colors are spliced, so that the influence of the single-color light source on the surfaces of the grey-scale maps is reduced, and the accuracy of the result is improved. According to the method, the surface friction coefficient of the wafer to be detected can be determined by splicing the grey-scale map (namely the weighted grey-scale map) and the standard grey-scale map, and then similarity comparison is performed on the spliced grey-scale map (namely the weighted grey-scale map) and the standard grey-scale map), so that the surface friction coefficient of the wafer to be detected can be determined, and the wafer surface friction coefficient can be relatively accurately detected through the method, thereby providing data support for subsequent processing. And unnecessary loss in the semiconductor manufacturing process can be reduced.

Description

Technical Field

[0001] The present application relates to the field of semiconductor technology, and in particular to a method and device for determining a friction coefficient of a wafer surface used in a semiconductor manufacturing process. Background Art

[0002] Currently in semiconductor equipment, semiconductor wafer transfer equipment uses the end effector in the semiconductor wafer transfer equipment to transfer the wafer during the wafer transmission process, and has high requirements for the transfer process, mainly to ensure the stability, safety and accuracy of the wafer during the transfer process, and to reduce the contamination and damage of the wafer surface. The end effector in the semiconductor wafer transfer equipment not only needs to have excellent corrosion resistance and high temperature resistance, but also requires to minimize the contamination and damage to the wafer. In addition to meeting the requirements of safe wafer transportation, it also adapts to the chemicals and high temperature environment in the semiconductor manufacturing process. In view of the above, the part of the end effector that contacts the wafer is usually made of alumina ceramic material.

[0003] At present, the friction coefficient of alumina ceramics on the market is usually between 0.2 and 0.4. When alumina ceramic materials are used to make the part where the end effector contacts the wafer, the side of the wafer that contacts the end effector must also reach a certain friction force. Otherwise, the wafer will move during the movement process, or even fall off the end effector, causing unnecessary losses. Therefore, it is very necessary to test the friction condition of the wafer before placing it on the end effector. Summary of the invention

[0004] In view of this, an embodiment of the present application provides a method and device for determining the friction coefficient of the wafer surface during the semiconductor manufacturing process, so as to determine the friction conditions of the wafer and reduce unnecessary losses during the semiconductor manufacturing process.

[0005] In a first aspect, an embodiment of the present application provides a method for determining a coefficient of friction of a wafer surface in a semiconductor manufacturing process, wherein the determination method is applied to a semiconductor device front module, wherein a shooting device is provided in the semiconductor device front module, wherein the shooting device includes a dark box, and a color camera and a plurality of color light sources arranged at the top of the dark box. After the overhead crane system places a wafer box on the semiconductor device front module, a wafer robot on the semiconductor device front module takes out a wafer to be tested from the wafer box and places it in the dark box with the contact surface of the wafer to be tested and the end effector facing upward, and controls light sources of different colors to light up in sequence according to preset durations, and when each light source is lit, the color camera is used to take a picture of the wafer to be tested according to preset shooting parameters. The method includes: After obtaining color images corresponding to light sources of different colors, for each color image, a grayscale image of the color image of that color is obtained; For the grayscale image corresponding to each color, the grayscale image is divided into multiple grayscale blocks according to a preset step size; For each grayscale block corresponding to a color, the following formula is used to determine the set of similar grayscale blocks of that color: ; in, B is the current block in the grayscale image corresponding to this color, A is the candidate block in the grayscale image corresponding to this color, p is the size of the grayscale block, Indicates the number of Line Grayscale values ​​on the columns, Represents the grayscale mean of the current block, Indicates the candidate block Line Grayscale values ​​on the columns, represents the grayscale mean of the candidate block, Indicates the grayscale standard deviation of the current block, Represents the grayscale standard deviation of the candidate block; For each similar grayscale block set, each grayscale image block in the similar grayscale block set is taken as a two-dimensional layer, and the grayscale image blocks in the similar grayscale block set are stacked to obtain a three-dimensional matrix corresponding to the similar grayscale block set; The three-dimensional matrix corresponding to the similar grayscale block set is denoised using the following formula: Hard , )= ; Where X is the gray value of each pixel in the three-dimensional matrix, and T is the preset gray threshold; Performing a three-dimensional inverse transformation on the three-dimensional matrix after denoising each similar grayscale block set to obtain a processed grayscale image corresponding to each similar grayscale block set; According to the position of the processed grayscale image in the grayscale image corresponding to the color, the processed grayscale images included in the sets of similar grayscale blocks corresponding to the color are spliced ​​to obtain a denoised grayscale image; After obtaining the denoised grayscale image corresponding to each color, weighted processing is performed on the denoised grayscale image corresponding to each color to obtain a weighted grayscale image; The weighted grayscale image and a preset standard grayscale image of a standard wafer are compared for similarity to determine the surface friction coefficient of the wafer to be tested.

[0006] Optionally, the light sources of multiple colors include: a white light source, a blue light source, a red light source and a green light source.

[0007] Optionally, the comparing the weighted grayscale image with a preset standard grayscale image of a standard wafer to determine the friction coefficient of the surface of the wafer to be tested includes: The similarity between the weighted grayscale image and the standard grayscale image is calculated using the following formula: ; Wherein, m is the weighted grayscale image, n is the standard grayscale image, is the average grayscale value of the weighted grayscale image, is the average grayscale value of the standard grayscale image, is the standard deviation of the grayscale value of the weighted grayscale image, is the standard deviation of the grayscale value of the standard grayscale image, The grayscale value covariance of the weighted grayscale image and the standard grayscale image, and is a constant less than 1; The product of the friction coefficient of the standard wafer and the similarity is calculated, and the product is used as the surface friction coefficient.

[0008] Optionally, the method further comprises: When the surface friction coefficient is greater than or equal to a preset friction coefficient, controlling the wafer robot arm to transfer the wafer to be tested to an end effector in a semiconductor wafer transfer device; When the surface friction coefficient is less than the preset friction coefficient, the wafer robot arm is controlled to transfer the wafer to be tested into a candidate wafer box.

[0009] Optionally, after determining the surface friction coefficient of the wafer to be tested, the method further includes: The roughness of the weighted grayscale image is determined using the following formula: ; in, represents the number of times the gray levels e and f appear simultaneously after the gray value of each pixel in the gray level co-occurrence matrix corresponding to the weighted gray level image is quantized, the gray level range of e and f is 0-7, H is the number of gray levels, represents the element value at the position (k, l) in the gray level co-occurrence matrix; When the roughness is greater than a preset roughness range, controlling the wafer robot arm to move the wafer to be tested into a first wafer box; When the roughness is less than the preset roughness range, controlling the wafer robot arm to move the wafer to be tested into a second wafer box; When the roughness is within the preset roughness range, the surface friction coefficient is compared with the preset friction coefficient.

[0010] In a second aspect, an embodiment of the present application provides a device for determining the friction coefficient of a wafer surface in a semiconductor manufacturing process, wherein the determining device is arranged in a front module of a semiconductor device, and a shooting device is arranged in the front module of the semiconductor device, wherein the shooting device includes a dark box, and a color camera and a plurality of color light sources arranged on the top of the dark box. After the overhead crane system places the wafer box on the front module of the semiconductor device, the wafer robot arm on the front module of the semiconductor device takes the wafer to be tested out of the wafer box and places it in the dark box with the contact surface of the wafer to be tested and the end effector facing upward, and controls the light sources of different colors to light up in sequence according to a preset duration, and when each light source is lit, the wafer to be tested is photographed by the color camera according to preset shooting parameters, and the determining device includes: An acquisition unit, configured to acquire a grayscale image of the color image of each color after acquiring color images corresponding to light sources of different colors; A segmentation unit, for segmenting a grayscale image corresponding to each color into a plurality of grayscale image blocks according to a preset step length; The first determination unit is used to determine a similar grayscale block set of each color for the grayscale image block corresponding to the color using the following formula: ; in, B is the current block in the grayscale image corresponding to this color, A is the candidate block in the grayscale image corresponding to this color, p is the size of the grayscale block, Indicates the number of Line Grayscale values ​​on the columns, Represents the grayscale mean of the current block, Indicates the candidate block Line Grayscale values ​​on the columns, represents the grayscale mean of the candidate block, Indicates the grayscale standard deviation of the current block, Represents the grayscale standard deviation of the candidate block; A stacking unit is used for stacking the grayscale blocks in each similar grayscale block set as a two-dimensional layer for each grayscale block set to obtain a three-dimensional matrix corresponding to the similar grayscale block set; The denoising unit is used to denoise the three-dimensional matrix corresponding to the set of similar grayscale blocks using the following formula: Hard , )= ; Where X is the gray value of each pixel in the three-dimensional matrix, and T is the preset gray threshold; A transformation unit, used for performing a three-dimensional inverse transformation on the three-dimensional matrix after denoising each similar grayscale block set, to obtain a processed grayscale image corresponding to each similar grayscale block set; A splicing unit, configured to splice the processed grayscale images included in the sets of similar grayscale blocks corresponding to the color according to the positions of the processed grayscale images in the grayscale image corresponding to the color, to obtain a denoised grayscale image; A weighting unit is used to perform weighted processing on the denoised grayscale images corresponding to each color after obtaining the denoised grayscale images corresponding to each color, so as to obtain a weighted grayscale image; The comparison unit is used to compare the weighted grayscale image with a preset standard grayscale image of a standard wafer to determine the surface friction coefficient of the wafer to be tested.

[0011] Optionally, the light sources of multiple colors include: a white light source, a blue light source, a red light source and a green light source.

[0012] Optionally, the comparison unit is used to compare the weighted grayscale image with a preset standard grayscale image of a standard wafer for similarity, and when determining the friction coefficient of the surface of the wafer to be tested, it includes: The similarity between the weighted grayscale image and the standard grayscale image is calculated using the following formula: ; Wherein, m is the weighted grayscale image, n is the standard grayscale image, is the average grayscale value of the weighted grayscale image, is the average grayscale value of the standard grayscale image, is the standard deviation of the grayscale value of the weighted grayscale image, is the standard deviation of the grayscale value of the standard grayscale image, The grayscale value covariance of the weighted grayscale image and the standard grayscale image, and is a constant less than 1; The product of the friction coefficient of the standard wafer and the similarity is calculated, and the product is used as the surface friction coefficient.

[0013] Optionally, the determining device further includes: A control unit is used to control the wafer robot to transfer the wafer to be tested to the end effector in the semiconductor wafer transfer device when the surface friction coefficient is greater than or equal to the preset friction coefficient; and to control the wafer robot to transfer the wafer to be tested to the candidate wafer box when the surface friction coefficient is less than the preset friction coefficient.

[0014] Optionally, the determining device further includes: The second determination unit is used to determine the roughness of the weighted grayscale image by using the following formula after determining the surface friction coefficient of the wafer to be tested: ; in, represents the number of times the gray levels e and f appear simultaneously after the gray value of each pixel in the gray level co-occurrence matrix corresponding to the weighted gray level image is quantized, the gray level range of e and f is 0-7, H is the number of gray levels, represents the element value at the position (k, l) in the gray level co-occurrence matrix; The control unit is also used to control the wafer robot to move the wafer to be tested into a first wafer box when the roughness is greater than a preset roughness range; to control the wafer robot to move the wafer to be tested into a second wafer box when the roughness is less than the preset roughness range; and to compare the surface friction coefficient with the preset friction coefficient when the roughness is within the preset roughness range.

[0015] The technical solution provided by the embodiments of the present application may have the following beneficial effects: In the present application, by shooting under the illumination of multiple color light sources, color images of multiple colors can be obtained, and then the grayscale images corresponding to the color images of each color are denoised through processing. Since the grayscale image can better show the characteristics of the wafer surface, and in order to avoid the influence of a single color light source on the result, the grayscale images corresponding to the multiple colors are spliced, which is beneficial to reduce the influence of a single color light source on the grayscale image surface. Then, the spliced ​​grayscale image (ie, weighted grayscale image) and the standard grayscale image are compared for similarity, so that the surface friction coefficient of the wafer to be tested can be determined. Through the above method, the friction coefficient of the wafer surface can be detected relatively accurately, thereby providing data support for subsequent processing, which is beneficial to reduce unnecessary losses in the semiconductor manufacturing process.

[0016] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic flow chart of a method for determining a friction coefficient of a wafer surface in a semiconductor manufacturing process provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a device for determining the friction coefficient of a wafer surface used in a semiconductor manufacturing process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] EFEM (Equipment Front End Module) is a key part of semiconductor equipment. It is mainly responsible for the transmission of wafers (silicon chips) between different devices and ensures the cleanliness and isolation of the process environment. In the semiconductor manufacturing process, EFEM plays a vital role. It can improve production efficiency, reduce pollution, and ensure product quality. EFEM is widely used in various links in semiconductor production lines, such as lithography, etching, ion implantation and other key process steps. In these links, EFEM can ensure that the wafer is not contaminated during the transmission process and improve the yield and reliability of the product.

[0021] Figure 1A flow chart of a method for determining the friction coefficient of a wafer surface in a semiconductor manufacturing process provided in an embodiment of the present application, wherein the determination method is applied to a front module of a semiconductor device (i.e., an EFEM device), wherein a shooting device is provided in the front module of the semiconductor device, wherein the shooting device includes a dark box, and a color camera and a plurality of color light sources arranged on the top of the dark box. After the overhead crane system places a wafer box on the front module of the semiconductor device, a wafer robot arm on the front module of the semiconductor device takes out a wafer to be tested from the wafer box and places it in the dark box with the contact surface of the wafer to be tested and the end effector facing upward, and controls light sources of different colors to light up in sequence according to preset durations, and when each light source is lit, the color camera is used to take a picture of the wafer to be tested according to preset shooting parameters, such as Figure 1 As shown, the method comprises the following steps: Step 101: After obtaining color images corresponding to light sources of different colors, for each color image, obtain a grayscale image of the color image of that color.

[0022] Step 102: For the grayscale image corresponding to each color, the grayscale image is divided into a plurality of grayscale blocks according to a preset step size.

[0023] Step 103: For each grayscale block corresponding to a color, determine a set of similar grayscale blocks of the color using the following formula 1: ; (Formula 1) in, B is the current block in the grayscale image corresponding to this color, A is the candidate block in the grayscale image corresponding to this color, p is the size of the grayscale block, Indicates the number of Line Grayscale values ​​on the columns, Represents the grayscale mean of the current block, Indicates the candidate block Line Grayscale values ​​on the columns, represents the grayscale mean of the candidate block, Indicates the grayscale standard deviation of the current block, Represents the grayscale standard deviation of the candidate block.

[0024] Step 104 : For each similar grayscale block set, take each grayscale image block in the similar grayscale block set as a two-dimensional layer, stack the grayscale image blocks in the similar grayscale block set, and obtain a three-dimensional matrix corresponding to the similar grayscale block set.

[0025] Step 105: De-noise the three-dimensional matrix corresponding to the similar grayscale block set using the following formula 2: Hard , )= ; (Formula 2) Wherein, X is the gray value of each pixel in the three-dimensional matrix, and T is the preset gray threshold.

[0026] Step 106: Perform a three-dimensional inverse transformation on the three-dimensional matrix after denoising each similar grayscale block set to obtain a processed grayscale image corresponding to each similar grayscale block set.

[0027] Step 107 , according to the position of the processed grayscale image in the grayscale image corresponding to the color, the processed grayscale images included in the similar grayscale block sets corresponding to the color are spliced ​​to obtain a denoised grayscale image.

[0028] Step 108: After obtaining the denoised grayscale image corresponding to each color, weighted processing is performed on the denoised grayscale image corresponding to each color to obtain a weighted grayscale image.

[0029] Step 109 , performing a similarity comparison between the weighted grayscale image and a preset standard grayscale image of a standard wafer to determine the surface friction coefficient of the wafer to be tested.

[0030] Specifically, a shooting device is provided on the EFEM equipment, and the shooting device includes a dark box. The wafer manipulator on the EFEM can move the wafer into the dark box for taking pictures, so as to obtain an image of the wafer surface. In order to avoid the influence of a single color light source on the wafer surface, a light source of multiple colors is provided in the dark box, so as to obtain a color image of the wafer surface taken under different color illuminations. After obtaining the color images corresponding to the light sources of different colors, the color images of each color are converted into a grayscale image, and then the grayscale image corresponding to each color image is divided into a plurality of grayscale blocks by a preset step size. For example, when the color image includes: color image 1, color image 2 and color image 3, the color image 1, color image 2 and color image 3 are respectively converted into grayscale images to obtain the grayscale image corresponding to the color image 1. Figure 1 , grayscale corresponding to color image 2 Figure 2 And the grayscale image 3 corresponding to the color image 3, and then the grayscale Figure 1 , Grayscale Figure 2 And grayscale image 3 is divided into multiple grayscale blocks, then the grayscale image can be obtained Figure 1 Corresponding multiple grayscale blocks 1, grayscale Figure 2 The corresponding multiple grayscale blocks 2 and the corresponding multiple grayscale blocks 3 of the grayscale image 3, for example: a step length of 3 pixels is used to segment the grayscale image, Taking multiple grayscale blocks 1 as an example, several current blocks are set in the multiple grayscale blocks 1, and formula 1 is used to determine the similar grayscale block sets of the multiple grayscale blocks 1, that is: formula 1 is used to search for several candidate blocks that are most similar to the current block in the area around the current block (the similarity can be determined by the Euclidean distance), so that multiple similar grayscale block sets 1 corresponding to the multiple grayscale blocks 1 can be obtained.

[0031] For example: when the similar grayscale block set includes: similar grayscale block set 1, similar grayscale block set 2 and similar grayscale block set 3, each grayscale block included in similar grayscale block set 1 is taken as a two-dimensional layer, and each grayscale block in similar grayscale block set 1 is stacked to obtain a three-dimensional matrix 1 of similar grayscale block set 1, and the above processing is also performed on similar grayscale block set 2 and similar grayscale block set 3 to obtain a three-dimensional matrix 3 of similar grayscale block set 2 and a three-dimensional matrix 3 of similar grayscale block set 3.

[0032] Then, the three-dimensional matrix 1, the three-dimensional matrix 2 and the three-dimensional matrix 3 are denoised by using formula 2 to obtain the denoised three-dimensional matrix 1, the denoised three-dimensional matrix 2 and the denoised three-dimensional matrix 3, and the denoised three-dimensional matrix 1, the denoised three-dimensional matrix 2 and the denoised three-dimensional matrix 3 are respectively subjected to three-dimensional inverse transformation to obtain multiple processed grayscale blocks corresponding to the similar grayscale block set 1. Figure 1 , multiple processing grayscales corresponding to similar grayscale block set 2 Figure 2 A plurality of processed grayscale images 3 corresponding to the similar grayscale block set 3, and then Figure 1 , multiple processing grayscale Figure 2 and multiple processing grayscale images 3 in grayscale Figure 1 The original position in the grayscale Figure 1 , multiple processing grayscale Figure 2 And multiple processed grayscale images 3 are spliced ​​to obtain grayscale Figure 1 Corresponding denoising grayscale Figure 1 , according to the above method, the grayscale Figure 2 Corresponding denoising grayscale Figure 2 Denoised grayscale image 3 corresponding to grayscale image 3.

[0033] Then, the denoising grayscale Figure 1 , Denoising Grayscale Figure 2 A weighted sum is performed on the denoised grayscale image 3 to obtain a weighted grayscale image, and a similarity comparison is performed between the weighted grayscale image and a standard grayscale image of a preset standard wafer to determine the surface friction coefficient of the wafer to be tested.

[0034] In the present application, by shooting under the illumination of multiple color light sources, color images of multiple colors can be obtained, and then the grayscale images corresponding to the color images of each color are denoised through processing. Since the grayscale image can better show the characteristics of the wafer surface, and in order to avoid the influence of a single color light source on the result, the grayscale images corresponding to the multiple colors are spliced, which is beneficial to reduce the influence of a single color light source on the grayscale image surface. Then, the spliced ​​grayscale image (ie, weighted grayscale image) and the standard grayscale image are compared for similarity, so that the surface friction coefficient of the wafer to be tested can be determined. Through the above method, the friction coefficient of the wafer surface can be detected relatively accurately, thereby providing data support for subsequent processing, which is beneficial to reduce unnecessary losses in the semiconductor manufacturing process.

[0035] It should be noted that the specific color and quantity of the light source can be set according to actual needs. For example, the color of the light source can be red, green, and blue, or red, green, blue and white, or red, white, and blue. When performing weighted summation, weights can be configured for different denoising grayscale images according to the specific color selected. For example, when the color of the light source is red, green, and blue, the weight of the denoising grayscale image configured for red can be 0.299, the weight of the denoising grayscale image configured for green can be 0.587, and the weight of the denoising grayscale image configured for blue can be 0.114. Of course, the specific weights can also be set according to actual needs, and no specific limitation is made here.

[0036] In a feasible embodiment, the light sources of multiple colors include: a white light source, a blue light source, a red light source and a green light source.

[0037] In a feasible implementation, when executing step 109, the similarity between the weighted grayscale image and the standard grayscale image may be calculated using the following formula 3: ; (Formula 3) Wherein, m is the weighted grayscale image, n is the standard grayscale image, is the average grayscale value of the weighted grayscale image, is the average grayscale value of the standard grayscale image, is the standard deviation of the grayscale value of the weighted grayscale image, is the standard deviation of the grayscale value of the standard grayscale image, The grayscale value covariance of the weighted grayscale image and the standard grayscale image, and is a constant less than 1.

[0038] Then, the product of the friction coefficient of the standard wafer and the similarity is calculated, and the product is used as the surface friction coefficient.

[0039] Specifically, the average gray value can reflect the brightness information of the grayscale image, the standard deviation of the grayscale value can reflect the contrast information of the grayscale image, and the grayscale value covariance can reflect the structural similarity between the weighted grayscale image and the standard grayscale image. and are constants introduced to avoid division by zero errors. Their values ​​are = , = , is the number of gray levels (for 8 grayscale images, ), The value can be 0.01. The value can be 0.03.

[0040] In a feasible embodiment, when the surface friction coefficient is greater than or equal to a preset friction coefficient, the wafer robot is controlled to transfer the wafer to be tested to the end effector in the semiconductor wafer transfer device; when the surface friction coefficient is less than the preset friction coefficient, the wafer robot is controlled to transfer the wafer to be tested to a candidate wafer box. Specifically, a wafer box specifically used for collecting the surface friction coefficient may be placed so as to place wafers with a smaller surface friction coefficient.

[0041] In a feasible implementation scheme, after determining the surface friction coefficient of the wafer to be tested, in order to avoid the wafer surface being too smooth or too rough, the roughness of the weighted grayscale image can be determined using the following formula 4: ; (Formula 4) in, represents the number of times the gray levels e and f appear simultaneously after the gray value of each pixel in the gray level co-occurrence matrix corresponding to the weighted gray level image is quantized, the gray level range of e and f is 0-7, H is the number of gray levels, Represents the element value at the (k, l) position in the gray-level co-occurrence matrix.

[0042] The above formula reflects the uniformity and smoothness of the grayscale distribution of the image. A higher value usually means a more uniform image texture, which may correspond to a smoother object surface; a lower energy value may correspond to a rougher object surface.

[0043] When the roughness is greater than a preset roughness range, controlling the wafer robot arm to move the wafer to be tested into a first wafer box; When the roughness is less than the preset roughness range, controlling the wafer robot arm to move the wafer to be tested into a second wafer box; When the roughness is within the preset roughness range, the surface friction coefficient is compared with the preset friction coefficient.

[0044] Figure 2 A schematic diagram of the structure of a device for determining the friction coefficient of a wafer surface used in a semiconductor manufacturing process provided in an embodiment of the present application, wherein the determining device is arranged in a front module of a semiconductor device, and a shooting device is arranged in the front module of the semiconductor device, and the shooting device includes a dark box, and a color camera and a plurality of color light sources arranged on the top of the dark box. After the overhead crane system places the wafer box on the front module of the semiconductor device, the wafer robot arm on the front module of the semiconductor device takes the wafer to be tested out of the wafer box and places it in the dark box with the contact surface of the wafer to be tested and the end effector facing upward, and controls the light sources of different colors to light up in sequence according to a preset duration, and when each light source is lit, the color camera is used to take a picture of the wafer to be tested according to preset shooting parameters, such as Figure 2 As shown, the determining device includes: An acquisition unit 21 is used to acquire a grayscale image of the color image of each color after acquiring color images corresponding to light sources of different colors; A segmentation unit 22 is used to segment the grayscale image corresponding to each color into a plurality of grayscale blocks according to a preset step size; The first determining unit 23 is used to determine a similar grayscale block set of each color for the grayscale block corresponding to the color using the following formula: ; in, B is the current block in the grayscale image corresponding to this color, A is the candidate block in the grayscale image corresponding to this color, p is the size of the grayscale block, Indicates the number of Line Grayscale values ​​on the columns, Represents the grayscale mean of the current block, Indicates the candidate block Line Grayscale values ​​on the columns, represents the grayscale mean of the candidate block, Indicates the grayscale standard deviation of the current block, Represents the grayscale standard deviation of the candidate block; A stacking unit 24 is used for stacking the grayscale blocks in each similar grayscale block set as a two-dimensional layer to obtain a three-dimensional matrix corresponding to the similar grayscale block set. The denoising unit 25 is used to denoise the three-dimensional matrix corresponding to the similar grayscale block set using the following formula: Hard , )= ; Where X is the gray value of each pixel in the three-dimensional matrix, and T is the preset gray threshold; A transformation unit 26 is used to perform a three-dimensional inverse transformation on the three-dimensional matrix after denoising each similar grayscale block set to obtain a processed grayscale image corresponding to each similar grayscale block set; A splicing unit 27 is used to splice the processed grayscale images included in each set of similar grayscale blocks corresponding to the color according to the position of the processed grayscale image in the grayscale image corresponding to the color, so as to obtain a denoised grayscale image; A weighting unit 28 is used to perform weighted processing on the denoised grayscale images corresponding to each color after obtaining the denoised grayscale images corresponding to each color to obtain a weighted grayscale image; The comparison unit 29 is used to compare the weighted grayscale image with a preset standard grayscale image of a standard wafer to determine the surface friction coefficient of the wafer to be tested.

[0045] In a feasible embodiment, the light sources of multiple colors include: a white light source, a blue light source, a red light source and a green light source.

[0046] In a feasible implementation manner, the comparison unit is used to compare the weighted grayscale image with a preset standard grayscale image of a standard wafer for similarity, and when determining the friction coefficient of the surface of the wafer to be tested, it includes: The similarity between the weighted grayscale image and the standard grayscale image is calculated using the following formula: ; Wherein, m is the weighted grayscale image, n is the standard grayscale image, is the average grayscale value of the weighted grayscale image, is the average grayscale value of the standard grayscale image, is the standard deviation of the grayscale value of the weighted grayscale image, is the standard deviation of the grayscale value of the standard grayscale image, The grayscale value covariance of the weighted grayscale image and the standard grayscale image, and is a constant less than 1; The product of the friction coefficient of the standard wafer and the similarity is calculated, and the product is used as the surface friction coefficient.

[0047] In a feasible implementation manner, the determining device further includes: A control unit is used to control the wafer robot to transfer the wafer to be tested to the end effector in the semiconductor wafer transfer device when the surface friction coefficient is greater than or equal to the preset friction coefficient; and to control the wafer robot to transfer the wafer to be tested to the candidate wafer box when the surface friction coefficient is less than the preset friction coefficient.

[0048] In a feasible implementation manner, the determining device further includes: The second determination unit is used to determine the roughness of the weighted grayscale image by using the following formula after determining the surface friction coefficient of the wafer to be tested: ; in, represents the number of times the gray levels e and f appear simultaneously after the gray value of each pixel in the gray level co-occurrence matrix corresponding to the weighted gray level image is quantized, the gray level range of e and f is 0-7, H is the number of gray levels, represents the element value at the position (k, l) in the gray level co-occurrence matrix; The control unit is also used to control the wafer robot to move the wafer to be tested into a first wafer box when the roughness is greater than a preset roughness range; to control the wafer robot to move the wafer to be tested into a second wafer box when the roughness is less than the preset roughness range; and to compare the surface friction coefficient with the preset friction coefficient when the roughness is within the preset roughness range.

[0049] about Figure 2 The principles of related content can be found in Figure 1 The explanation of the relevant content will not be repeated in detail here.

[0050] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0051] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0052] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0053] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0054] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0055] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for determining the friction coefficient of a wafer surface in a semiconductor manufacturing process, characterized in that: The determination method is applied to a semiconductor device front module, wherein a shooting device is provided in the semiconductor device front module, wherein the shooting device includes a dark box, and a color camera and a plurality of color light sources provided on the top of the dark box. After the overhead crane system places a wafer box on the semiconductor device front module, a wafer robot on the semiconductor device front module takes out a wafer to be tested from the wafer box and places it in the dark box with the contact surface of the wafer to be tested and the end effector facing upward, and controls light sources of different colors to light up in sequence according to preset durations, and when each light source is lit, takes a picture of the wafer to be tested by the color camera according to preset shooting parameters. The method includes: After obtaining color images corresponding to light sources of different colors, for each color image, a grayscale image of the color image of that color is obtained; For the grayscale image corresponding to each color, the grayscale image is divided into multiple grayscale blocks according to a preset step size; For each grayscale block corresponding to a color, the following formula is used to determine the set of similar grayscale blocks of that color: ； in, B is the current block in the grayscale image corresponding to this color, A is the candidate block in the grayscale image corresponding to this color, p is the size of the grayscale block, Indicates the number of Line Grayscale values ​​on the columns, Represents the grayscale mean of the current block, Indicates the candidate block Line Grayscale values ​​on the columns, represents the grayscale mean of the candidate block, Indicates the grayscale standard deviation of the current block, Represents the grayscale standard deviation of the candidate block; For each similar grayscale block set, each grayscale image block in the similar grayscale block set is taken as a two-dimensional layer, and the grayscale image blocks in the similar grayscale block set are stacked to obtain a three-dimensional matrix corresponding to the similar grayscale block set; The three-dimensional matrix corresponding to the similar grayscale block set is denoised using the following formula: Hard( , )= ; Where X is the gray value of each pixel in the three-dimensional matrix, and T is the preset gray threshold; Performing a three-dimensional inverse transformation on the three-dimensional matrix after denoising each similar grayscale block set to obtain a processed grayscale image corresponding to each similar grayscale block set; According to the position of the processed grayscale image in the grayscale image corresponding to the color, the processed grayscale images included in the sets of similar grayscale blocks corresponding to the color are spliced ​​to obtain a denoised grayscale image; After obtaining the denoised grayscale image corresponding to each color, weighted processing is performed on the denoised grayscale image corresponding to each color to obtain a weighted grayscale image; The weighted grayscale image and a preset standard grayscale image of a standard wafer are compared for similarity to determine the surface friction coefficient of the wafer to be tested.

2. The determination method according to claim 1, characterized in that: The light sources of multiple colors include: a white light source, a blue light source, a red light source and a green light source.

3. The determination method according to claim 1, characterized in that: The comparing the similarity between the weighted grayscale image and a preset standard grayscale image of a standard wafer to determine the friction coefficient of the surface of the wafer to be tested includes: The similarity between the weighted grayscale image and the standard grayscale image is calculated using the following formula: ； Wherein, m is the weighted grayscale image, n is the standard grayscale image, is the average grayscale value of the weighted grayscale image, is the average grayscale value of the standard grayscale image, is the standard deviation of the grayscale value of the weighted grayscale image, is the standard deviation of the grayscale value of the standard grayscale image, The grayscale value covariance of the weighted grayscale image and the standard grayscale image, and is a constant less than 1; The product of the friction coefficient of the standard wafer and the similarity is calculated, and the product is used as the surface friction coefficient.

4. The determination method according to claim 1, characterized in that: The method further comprises: When the surface friction coefficient is greater than or equal to a preset friction coefficient, controlling the wafer robot arm to transfer the wafer to be tested to an end effector in a semiconductor wafer transfer device; When the surface friction coefficient is less than the preset friction coefficient, the wafer robot arm is controlled to transfer the wafer to be tested into a candidate wafer box.

5. The determination method according to claim 4, characterized in that: After determining the surface friction coefficient of the wafer to be tested, the method further includes: The roughness of the weighted grayscale image is determined using the following formula: ； in, represents the number of times the gray levels e and f appear simultaneously after the gray value of each pixel in the gray level co-occurrence matrix corresponding to the weighted gray level image is quantized, the gray level range of e and f is 0-7, H is the number of gray levels, represents the element value at the position (k, l) in the gray level co-occurrence matrix; When the roughness is greater than a preset roughness range, controlling the wafer robot arm to move the wafer to be tested into a first wafer box; When the roughness is less than the preset roughness range, controlling the wafer robot arm to move the wafer to be tested into a second wafer box; When the roughness is within the preset roughness range, the surface friction coefficient is compared with the preset friction coefficient.

6. A device for determining the friction coefficient of a wafer surface in a semiconductor manufacturing process, characterized in that: The determining device is arranged in a semiconductor device front module, and a shooting device is arranged in the semiconductor device front module, and the shooting device includes a dark box, and a color camera and a plurality of color light sources arranged on the top of the dark box. After the overhead crane system places the wafer box on the semiconductor device front module, the wafer robot on the semiconductor device front module takes the wafer to be tested out of the wafer box and places it in the dark box with the contact surface of the wafer to be tested and the end effector facing upward, and controls the light sources of different colors to light up in sequence according to the preset duration, and when each light source is lit, the wafer to be tested is photographed by the color camera according to the preset shooting parameters. The determining device includes: An acquisition unit, configured to acquire a grayscale image of the color image of each color after acquiring color images corresponding to light sources of different colors; A segmentation unit, for segmenting a grayscale image corresponding to each color into a plurality of grayscale image blocks according to a preset step length; The first determination unit is used to determine a similar grayscale block set of each color for the grayscale image block corresponding to the color using the following formula: ； in, B is the current block in the grayscale image corresponding to this color, A is the candidate block in the grayscale image corresponding to this color, p is the size of the grayscale block, Indicates the number of Line Grayscale values ​​on the columns, Represents the grayscale mean of the current block, Indicates the candidate block Line Grayscale values ​​on the columns, represents the grayscale mean of the candidate block, Indicates the grayscale standard deviation of the current block, Represents the grayscale standard deviation of the candidate block; A stacking unit is used for stacking the grayscale blocks in each similar grayscale block set as a two-dimensional layer for each grayscale block set to obtain a three-dimensional matrix corresponding to the similar grayscale block set; The denoising unit is used to denoise the three-dimensional matrix corresponding to the set of similar grayscale blocks using the following formula: Hard( , )= ; Where X is the gray value of each pixel in the three-dimensional matrix, and T is the preset gray threshold; A transformation unit, used for performing a three-dimensional inverse transformation on the three-dimensional matrix after denoising each similar grayscale block set, to obtain a processed grayscale image corresponding to each similar grayscale block set; A splicing unit, configured to splice the processed grayscale images included in the sets of similar grayscale blocks corresponding to the color according to the positions of the processed grayscale images in the grayscale image corresponding to the color, to obtain a denoised grayscale image; A weighting unit is used to perform weighted processing on the denoised grayscale images corresponding to each color after obtaining the denoised grayscale images corresponding to each color, so as to obtain a weighted grayscale image; The comparison unit is used to compare the weighted grayscale image with a preset standard grayscale image of a standard wafer to determine the surface friction coefficient of the wafer to be tested.

7. The determination device according to claim 6, characterized in that: The light sources of multiple colors include: a white light source, a blue light source, a red light source and a green light source.

8. The determination device according to claim 6, characterized in that: The comparison unit is used to compare the weighted grayscale image with a preset standard grayscale image of a standard wafer for similarity, and when determining the friction coefficient of the surface of the wafer to be tested, it includes: The similarity between the weighted grayscale image and the standard grayscale image is calculated using the following formula: ； Wherein, m is the weighted grayscale image, n is the standard grayscale image, is the average grayscale value of the weighted grayscale image, is the average grayscale value of the standard grayscale image, is the standard deviation of the grayscale value of the weighted grayscale image, is the standard deviation of the grayscale value of the standard grayscale image, The grayscale value covariance of the weighted grayscale image and the standard grayscale image, and is a constant less than 1; The product of the friction coefficient of the standard wafer and the similarity is calculated, and the product is used as the surface friction coefficient.

9. The determination device according to claim 6, characterized in that: The determining device also includes: A control unit is used to control the wafer robot to transfer the wafer to be tested to the end effector in the semiconductor wafer transfer device when the surface friction coefficient is greater than or equal to the preset friction coefficient; and to control the wafer robot to transfer the wafer to be tested to the candidate wafer box when the surface friction coefficient is less than the preset friction coefficient.

10. The determination device according to claim 9, characterized in that: The determining device also includes: The second determination unit is used to determine the roughness of the weighted grayscale image by using the following formula after determining the surface friction coefficient of the wafer to be tested: ； in, represents the number of times the gray levels e and f appear simultaneously after the gray value of each pixel in the gray level co-occurrence matrix corresponding to the weighted gray level image is quantized, the gray level range of e and f is 0-7, H is the number of gray levels, represents the element value at the position (k, l) in the gray level co-occurrence matrix; The control unit is also used to control the wafer robot to move the wafer to be tested into a first wafer box when the roughness is greater than a preset roughness range; to control the wafer robot to move the wafer to be tested into a second wafer box when the roughness is less than the preset roughness range; and to compare the surface friction coefficient with the preset friction coefficient when the roughness is within the preset roughness range.

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