Visual intelligent adjustment method and system of full-automatic die bonder

Abstract

The invention discloses a visual intelligent adjustment method and system of a full-automatic die bonder, and relates to the field of semiconductor back-end packaging manufacturing, and the method comprises the steps: collecting an image of a to-be-recognized feature in real time based on a camera in the full-automatic die bonder, and obtaining a real-time image; determining the optimal light source and the optimal brightness of the to-be-recognized features based on various light sources and real-time images in the full-automatic chip loader; obtaining a to-be-recognized feature image under the optimal light source and the optimal brightness to obtain an optimal image, and determining a template area of the optimal image; determining a region of interest of the optimal image based on the template region of the optimal image; based on the region of interest of the optimal image, cutting the optimal image to obtain the image of interest of the to-be-identified features; and the camera is adjusted based on the interested image of the to-be-identified features. Through the interested image, the position and angle of the to-be-identified feature are accurately obtained, the operation precision of the to-be-identified feature is improved, and the loading quality is improved.

Description

Technical Field

[0001] The present application relates to the field of semiconductor back-end packaging manufacturing technology, and in particular to a visual intelligent adjustment method and system for a fully automatic chip loader. Background Art

[0002] Existing semiconductor back-end packaging wafer loading machines often rely on manual adjustment of lighting, manual template making, and manual zooming during wafer loading. They have problems such as complex operation, low precision, low efficiency, difficulty in judging quality, and some cannot save and call recipe management. At the same time, due to errors in manual operation, the loading position and angle accuracy may be unstable, affecting the quality and performance of semiconductor equipment loading. Therefore, how to realize the intelligent vision of the wafer loading machine, improve the stability and quality of the loading position and angle accuracy, improve the efficiency of visual replacement of different varieties, and reduce the errors in manual operation are problems that need to be solved in the current back-end packaging equipment field. Summary of the invention

[0003] The purpose of this application is to provide a visual intelligent adjustment method and system for a fully automatic film loading machine, which can accurately obtain the position and angle of the feature to be identified based on a clear image of the feature to be identified, improve the operating accuracy of the feature to be identified, and improve the quality of loading.

[0004] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a visual intelligent adjustment method for a fully automatic loader, comprising: Based on the camera in the fully automatic wafer mounter, the image of the feature to be identified is collected in real time to obtain a real-time image; the feature to be identified is a wafer or a substrate.

[0005] Based on the multiple light sources in the full-automatic loader and the real-time image, the optimal light source and the optimal brightness of the feature to be identified are determined.

[0006] Acquire the feature image to be identified under the optimal light source and optimal brightness, obtain the optimal image, and determine the template area of ​​the optimal image.

[0007] Based on the template region of the optimal image, a region of interest of the optimal image is determined.

[0008] Based on the region of interest of the optimal image, the optimal image is cropped to obtain an image of interest of the feature to be identified; and the camera is adjusted based on the image of interest of the feature to be identified.

[0009] In a second aspect, the present application provides a visual intelligent adjustment system for a fully automatic loader, comprising: The image acquisition module is used to acquire the image of the feature to be identified in real time based on the camera in the full-automatic wafer loader to obtain a real-time image; the feature to be identified is a wafer or a substrate.

[0010] The optimal light source and optimal brightness determination module is connected to the image acquisition module and is used to determine the optimal light source and optimal brightness of the feature to be identified based on the multiple light sources in the full-automatic loader and the real-time image.

[0011] The template area determination module is connected to the optimal light source and optimal brightness determination module, and is used to obtain the feature image to be identified under the optimal light source and optimal brightness, obtain the optimal image, and determine the template area of ​​the optimal image.

[0012] The region of interest determination module is connected to the template region determination module and is used to determine the region of interest of the optimal image based on the template region of the optimal image.

[0013] The adjustment module is connected to the region of interest determination module and is used to crop the optimal image based on the region of interest of the optimal image to obtain an image of interest of the feature to be identified; and adjust the camera based on the image of interest of the feature to be identified.

[0014] According to the specific embodiments provided in this application, this application has the following technical effects: The present application provides a visual intelligent adjustment method and system for a fully automatic loader. Based on the camera in the fully automatic loader, the image of the wafer or substrate in the fully automatic loader is collected in real time, and rapid feedback is provided when determining the optimal light source and the optimal brightness, thereby improving work efficiency. At the same time, the tediousness of manual adjustment is solved, and manual adjustment errors are avoided. The optimal image is obtained and the template area of ​​the optimal image is determined, thereby avoiding the difference in the center position of the template when different operators select the template area; by determining the area of ​​interest, the optimal field of view is achieved. The camera is adjusted by obtaining the image of interest, so that the collected image of the wafer or substrate is clearer and more accurate. The present application accurately obtains the position and angle of the feature to be identified through the image of interest, thereby improving the subsequent operation accuracy of the feature to be identified and improving the quality of the loading. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the structure of a fully automatic wafer loading machine in one embodiment of the present application.

[0017] Figure 2 It is a schematic diagram of the structure of the camera unit in the fully automatic film loading machine in one embodiment of the present application.

[0018] Figure 3 This is a diagram of the application environment of a visual intelligent adjustment method for a fully automatic wafer loading machine in one embodiment of the present application.

[0019] Figure 4 A flowchart of a visual intelligent adjustment method for a fully automatic wafer loading machine provided in one embodiment of the present application.

[0020] Figure 5 A schematic diagram of the functional modules of a visual intelligent adjustment system for a fully automatic wafer loader provided in one embodiment of the present application.

[0021] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application.

[0022] Reference numerals: Loading section—1, track conveying section—2, glue dispensing section—3, glue dispensing camera section—4, bonding section—5, bonding camera section—6, wafer camera section—7, wafer platform—8, ejector section—9, frame section—10, unloading section—11, high-pixel camera—12, large-field-of-view and high-precision lens barrel—13, and a variety of matching light sources—14. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1 As shown, the fully automatic chip mounter includes a loading part 1, a track conveying part 2, a glue dispensing part 3, a glue dispensing camera part 4, a bonding part 5, a bonding camera part 6, a wafer camera part 7, a wafer platform 8, a pin part 9, a frame part 10, a unloading part 11 and a glue dispensing pad.

[0026] Among them, the loading part 1 transfers the substrate to the production line. The track conveying part 2 conveys the substrate in the track through the clamp. The glue dispensing part 3 applies glue to the substrate. The glue dispensing camera part 4 locates the specific position of a single pad on the substrate when the substrate is conveyed to the camera part. The glue dispensing pad supports the frame when dispensing or painting glue on the substrate. The bonding part 5 bonds the wafer to the frame. The bonding camera part 6 locates the specific position of the pad on the substrate when the substrate is conveyed to the bonding part. The wafer camera part 7 locates the specific position of the chip on the wafer. The wafer platform 8 moves the wafer in the X, Y and R directions. The ejector part 9 lifts the chip from the wafer so that the bonding head can take the chip away. The unloading part 11 removes the packaged devices from the production line.

[0027] The main working principle of the fully automatic chip mounter is: after the wafer positioned by the wafer camera unit 7 is sucked from the wafer platform 8, it is mounted on the lead frame or substrate that has been positioned by the bonding camera unit 6 and glued (or tinned, or dipped in flux). This application makes mounting more accurate and improves the quality of chip mounting by obtaining clear and accurate images of wafers and substrates.

[0028] The dispensing camera unit 4, the binding camera unit 6 and the wafer camera unit 7 of the fully automatic wafer loading machine adopt the intelligent vision adjustment method of the fully automatic wafer loading machine to realize the intelligent vision of the fully automatic wafer loading machine. The dispensing camera unit 4, the binding camera unit 6 and the wafer camera unit 7 are mainly composed of Figure 2 The device shown is composed of a high-pixel camera 12 , a large-viewing-field high-precision lens barrel 13 , and a matching variety of light sources 14 .

[0029] The visual intelligent adjustment method of the fully automatic wafer loading machine provided in the embodiment of the present application can be applied to Figure 3In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the image of the feature to be identified to the server 104. After the server 104 receives the image of the feature to be identified, the server 104 obtains a real-time image for the image of the feature to be identified, and determines the optimal light source and optimal brightness of the feature to be identified based on the multiple light sources in the fully automatic film loader and the real-time image; obtains the image of the feature to be identified under the optimal light source and optimal brightness, obtains the optimal image, and determines the template area of ​​the optimal image; based on the template area of ​​the optimal image, determines the region of interest of the optimal image; based on the region of interest of the optimal image, the optimal image is cropped to obtain the image of interest of the feature to be identified; and the camera is adjusted based on the image of interest of the feature to be identified. The server 104 can feedback the image of the feature to be identified collected after adjustment to the terminal 102. In addition, in some embodiments, the visual intelligent adjustment method of the fully automatic loader can also be implemented by the server 104 or the terminal 102 alone.

[0030] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0031] In an exemplary embodiment, Figure 4 As shown, a visual intelligent adjustment method for a fully automatic film loading machine is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 205. Among them: Step 201, based on the camera in the full-automatic wafer loader, collect the image of the feature to be identified in real time to obtain a real-time image; the feature to be identified is a wafer or a substrate.

[0032] Step 202, based on the multiple light sources in the full-automatic loader and the real-time image, determine the optimal light source and the optimal brightness of the feature to be identified.

[0033] Step 203, acquiring a feature image to be identified under an optimal light source and optimal brightness, obtaining an optimal image, and determining a template region of the optimal image.

[0034] Step 204: determine the region of interest of the optimal image based on the template region of the optimal image.

[0035] Step 205 , based on the region of interest of the optimal image, the optimal image is cropped to obtain an image of interest of the feature to be identified; and the camera is adjusted based on the image of interest of the feature to be identified.

[0036] In another exemplary embodiment of the present application, the above step 202 specifically includes: based on multiple light sources and real-time images in the fully automatic loader, the golden section search algorithm and the bandpass algorithm are used to determine the optimal light source and optimal brightness of the feature to be identified. The fully automatic loader can automatically adjust the brightness, color temperature and other parameters of the light according to the material, color and other characteristics of the wafer and lead frame, ensuring that the loader vision system can accurately capture the image information of the wafer and lead frame pins or base islands. Specifically including the following steps 301 to 306.

[0037] Step 301, for the nth iteration of any light source, based on the nth brightness interval of the light source, a golden section search algorithm is used to determine a first brightness in the nth brightness interval and a second brightness in the nth brightness interval. n>0. In the present application, the first brightness interval is [0,255], and the golden section ratio is generally 0.618. The first brightness and the second brightness in the first brightness interval are determined by the golden section ratio.

[0038] Step 302: Acquire a feature image to be identified at a first brightness in an n-th brightness interval and a feature image to be identified at a second brightness in an n-th brightness interval.

[0039] Step 303, based on the feature image to be identified at the first brightness in the nth brightness interval and the feature image to be identified at the second brightness in the nth brightness interval, a bandpass algorithm is used to determine the clarity of the feature image to be identified at the first brightness in the nth brightness interval and the clarity of the feature image to be identified at the second brightness in the nth brightness interval.

[0040] Step 304 : determining an (n+1)th brightness interval based on the definition and according to the nth brightness interval, the first brightness in the nth brightness interval, and the second brightness in the nth brightness interval.

[0041] Step 305, determine whether n is less than the total number of iterations. If so, perform the (n+1)th iteration; if not, determine the maximum clarity of the light source based on the clarity of the feature image to be identified at the first brightness in the first brightness interval to the nth brightness interval and the clarity of the feature image to be identified at the second brightness.

[0042] Step 306: Determine the optimal light source and optimal brightness of the feature to be identified based on the maximum clarity values ​​of the multiple light sources.

[0043] In another exemplary embodiment of the present application, there are several indicators for evaluating image clarity in the current image processing algorithm: 1. Gradient Magnitude: Gradient magnitude indicates the rate at which pixel values ​​in an image change. A clear image usually has more edges and details, so its gradient magnitude is larger. The clarity of an image can be evaluated by calculating its gradient. A larger gradient magnitude usually indicates a clearer image.

[0044] 2. Energy spectrum: The energy spectrum represents the energy distribution of different spatial frequencies in the image. A clear image usually has more high-frequency components, so its energy spectrum has higher energy in the high-frequency part. The energy spectrum can be obtained by calculating the Fourier transform of the image, and the clarity of the image can be evaluated accordingly.

[0045] In the present application, in the above step 303, based on the feature image to be identified at the first brightness in the nth brightness interval, a bandpass algorithm is used to determine the clarity of the feature image to be identified at the first brightness in the nth brightness interval, specifically including: Based on the feature image to be identified at the first brightness in the nth brightness interval, a frequency response curve of the feature image to be identified at the first brightness in the nth brightness interval is obtained by adopting fast Fourier transform.

[0046] The frequency response curve is integrated within a set frequency range to obtain the total energy of the feature image to be identified at the first brightness in the nth brightness interval within the set frequency range.

[0047] The total energy within the set frequency range is normalized to obtain the clarity of the feature image to be identified at the first brightness in the nth brightness interval.

[0048] Specifically, in practical applications, a bandpass filter is used to set the frequency range. A bandpass filter is a frequency domain filter that selectively retains information within a specific frequency range while suppressing signals of other frequencies. It is usually used to remove specific frequency noise in an image or enhance specific frequency components.

[0049] The bandpass filter implementation in this application is: .

[0050] in, The midpoint in the frequency domain The filter response at The midpoint in the frequency domain Distance to the center, is the standard deviation of the Gaussian distribution, is the radial center of the bandwidth.

[0051] The calculation formula for the total energy within the set frequency range in this application is: .

[0052] in, is the frequency spectrum representation of the signal, and are the two cutoff frequencies of the filter, is the total energy within the set frequency range.

[0053] The normalized calculation formula in this application is: .

[0054] in, is the normalized clarity.

[0055] In another exemplary embodiment of the present application, step 203 specifically includes: acquiring a feature image to be identified under an optimal light source and optimal brightness to obtain an optimal image.

[0056] Based on the optimal image, the template area of ​​the optimal image is determined by using the autocorrelation algorithm and the extreme value search algorithm.

[0057] Step 401: Based on the optimal image, an autocorrelation algorithm is used to obtain an autocorrelation image of the optimal image.

[0058] The autocorrelation of the autocorrelation algorithm refers to the degree of correlation between a signal (or image) and its copies at different time or spatial locations. In image processing, autocorrelation is usually used to measure the similarity or repeatability between a region in an image and its adjacent regions.

[0059] To determine the image signal and The difference or similarity is usually expressed by the autocorrelation function: .

[0060] in, is the autocorrelation function, for The image signal at the moment, for The image signal at the moment, is the offset.

[0061] Characteristics of autocorrelation function: Autocorrelation function is an even function ,when When it is 0, the autocorrelation function is equal to the energy of the signal is the maximum value of the autocorrelation function.

[0062] In image processing, the steps to obtain the autocorrelation image are as follows: 1. Fourier transform. By performing Fourier transform on the input image, it is converted from the spatial domain to the frequency domain. 2. Frequency domain image calculation. Based on the two obtained frequency domain images, the two frequency domain images are point-multiplied to obtain the convolution image. 3. Inverse Fourier transform. Based on the convolution image, it is converted to the spatial domain through inverse Fourier transform to obtain the autocorrelation image of the input image.

[0063] Step 402: Based on the autocorrelation image of the optimal image, an extreme value search algorithm is used to obtain the positions of multiple final feature points in the autocorrelation image.

[0064] Step 403: Determine the center position of the autocorrelation image based on the positions of the multiple final feature points in the autocorrelation image.

[0065] Step 404: determine the template region of the optimal image based on the center position of the autocorrelation image and the positions of four adjacent points.

[0066] In another exemplary embodiment of the present application, step 402 specifically includes: obtaining a plurality of pixel maximum points of the autocorrelation image based on the autocorrelation image of the optimal image.

[0067] Based on multiple pixel maximum points, the sub-pixel interpolation method is used to obtain the positions of multiple feature points.

[0068] Based on multiple pixel maximum points and set thresholds, multiple feature points are screened to obtain the positions of multiple final feature points in the autocorrelation image.

[0069] In another exemplary embodiment of the present application, the conventional method of changing the camera field of view is: combining a small-target camera with a variable-magnification lens, and modifying the field of view by controlling the lens to change the magnification. This method has the problem of too low lens resolution at high magnification.

[0070] In this application, a large-surface camera (2448*2048 pixel) can be combined with multiple groups of fixed-magnification lenses to change the camera's field of view by cutting the camera's own resolution. And the switching of the field of view does not affect the accuracy of the visual system itself and the lens resolution capability, thereby improving the overall visual accuracy. At the same time, the camera is calibrated at full resolution by pre-calibration, and even if the resolution is cut, it does not need to be recalibrated, thereby improving the switching efficiency. And this application can realize stepless transformation, by automatically obtaining the size of the wafer or the feature to be identified, automatically calculating the field of view, and obtaining the appropriate resolution, without wasting the field of view.

[0071] Step 204 in the present application specifically includes: determining the lengths of the sides of the template region based on the template region of the optimal image.

[0072] The region of interest of the optimal image is determined by setting multiples of the length of each side of the template area.

[0073] In another exemplary embodiment of the present application, step 205 specifically includes: determining the length and width of the region of interest based on the size of the region of interest of the optimal image.

[0074] Based on the length and width of the region of interest, the optimal image is cropped using a calibrated camera to obtain an image of interest with features to be identified.

[0075] The present application improves the stability and quality of the loading position and angle accuracy through the visual intelligent adjustment method of the fully automatic loading machine, improves the efficiency of visual replacement of different varieties, reduces the error of manual operation, and ensures the quality and performance of semiconductor products.

[0076] Based on the same inventive concept, Figure 5 As shown, the embodiment of the present application also provides a visual intelligent adjustment system for a fully automatic wafer loading machine. It includes: The image acquisition module 501 is used to acquire the image of the feature to be identified in real time based on the camera in the full-automatic wafer loader to obtain a real-time image; the feature to be identified is a wafer or a substrate.

[0077] The optimal light source and optimal brightness determination module 502 is connected to the image acquisition module 501 and is used to determine the optimal light source and optimal brightness of the feature to be identified based on the multiple light sources in the automatic loader and the real-time image.

[0078] The template region determination module 503 is connected to the optimal light source and optimal brightness determination module 502, and is used to obtain the feature image to be identified under the optimal light source and optimal brightness, obtain the optimal image, and determine the template region of the optimal image.

[0079] The region of interest determination module 504 is connected to the template region determination module 503 and is used to determine the region of interest of the optimal image based on the template region of the optimal image.

[0080] The adjustment module 505 is connected to the region of interest determination module 504 and is used to crop the optimal image based on the region of interest of the optimal image to obtain an image of interest of the feature to be identified; and adjust the camera to be identified based on the image of interest of the feature to be identified.

[0081] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store video tag processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a visual intelligent adjustment method of a fully automatic film loader is realized.

[0082] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0083] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0084] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0085] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0086] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A visual intelligent adjustment method for a fully automatic wafer loading machine, characterized in that: The visual intelligent adjustment method of the fully automatic loader comprises: Based on the camera in the fully automatic wafer mounter, an image of the feature to be identified is collected in real time to obtain a real-time image; the feature to be identified is a wafer or a substrate; Based on the multiple light sources in the fully automatic loader and the real-time image, the optimal light source and the optimal brightness of the feature to be identified are determined, specifically including: Based on the multiple light sources in the fully automatic loader and the real-time image, the optimal light source and the optimal brightness of the feature to be identified are determined by using the golden section search algorithm and the bandpass algorithm, specifically including: For the nth iteration of any light source, based on the nth brightness interval of the light source, a golden section search algorithm is used to determine a first brightness in the nth brightness interval and a second brightness in the nth brightness interval; n>0; Acquire a feature image to be identified at a first brightness in an nth brightness interval and a feature image to be identified at a second brightness in an nth brightness interval; Based on the feature image to be identified at the first brightness in the nth brightness interval and the feature image to be identified at the second brightness in the nth brightness interval, a bandpass algorithm is used to determine the clarity of the feature image to be identified at the first brightness in the nth brightness interval and the clarity of the feature image to be identified at the second brightness in the nth brightness interval; Based on the definition, determining an n+1th brightness interval according to an nth brightness interval, a first brightness in the nth brightness interval, and a second brightness in the nth brightness interval; Determine whether n is less than the total number of iterations, and if so, perform the n+1th iteration; if not, determine the maximum clarity value of the light source based on the clarity of the feature image to be identified at the first brightness and the clarity of the feature image to be identified at the second brightness in the first brightness interval to the nth brightness interval; Determining the optimal light source and optimal brightness of the feature to be identified based on the maximum clarity values ​​of the multiple light sources; Acquire the feature image to be identified under the optimal light source and optimal brightness, obtain the optimal image, and determine the template area of ​​the optimal image, specifically including: Acquire the feature image to be identified under the optimal light source and optimal brightness to obtain the optimal image; Based on the optimal image, the template area of ​​the optimal image is determined by using the autocorrelation algorithm and the extreme value search algorithm; Based on the template area of ​​the optimal image, the region of interest of the optimal image is determined, specifically including: Based on the template area of ​​the optimal image, determine the length of each side of the template area; Determine the region of interest of the optimal image by setting multiples of the lengths of each side of the template region; Based on the region of interest of the optimal image, the optimal image is cropped to obtain an image of interest of the feature to be identified; and the camera is adjusted based on the image of interest of the feature to be identified.

2. The visual intelligent adjustment method of the fully automatic wafer loading machine according to claim 1, characterized in that: Based on the feature image to be identified at the first brightness in the nth brightness interval, a bandpass algorithm is used to determine the clarity of the feature image to be identified at the first brightness in the nth brightness interval, specifically including: Based on the feature image to be identified at the first brightness in the nth brightness interval, a frequency response curve of the feature image to be identified at the first brightness in the nth brightness interval is obtained by using a fast Fourier transform; Integrate the frequency response curve within a set frequency range to obtain the total energy of the feature image to be identified at the first brightness in the nth brightness interval within the set frequency range; The total energy within the set frequency range is normalized to obtain the clarity of the feature image to be identified at the first brightness in the nth brightness interval.

3. The visual intelligent adjustment method of the fully automatic loader according to claim 1, characterized in that: Based on the optimal image, the template area of ​​the optimal image is determined using the autocorrelation algorithm and the extreme value search algorithm, including: Based on the optimal image, an autocorrelation algorithm is used to obtain an autocorrelation image of the optimal image; Based on the autocorrelation image of the optimal image, an extreme value search algorithm is used to obtain the positions of multiple final feature points in the autocorrelation image; Determine the center position of the autocorrelation image based on the positions of the multiple final feature points in the autocorrelation image; Based on the center position of the autocorrelation image and the positions of four adjacent points, the template area of ​​the optimal image is determined.

4. The visual intelligent adjustment method of the fully automatic wafer loading machine according to claim 3 is characterized in that: Based on the autocorrelation image of the optimal image, an extreme value search algorithm is used to obtain the positions of multiple final feature points in the autocorrelation image, including: Based on the autocorrelation image of the optimal image, multiple pixel maximum points of the autocorrelation image are obtained; Based on multiple pixel maximum points, the sub-pixel interpolation method is used to obtain the positions of multiple feature points; Based on multiple pixel maximum points and set thresholds, multiple feature points are screened to obtain the positions of multiple final feature points in the autocorrelation image.

5. The visual intelligent adjustment method of the fully automatic loader according to claim 1, characterized in that: Based on the region of interest of the optimal image, the optimal image is cropped to obtain an image of interest of the feature to be identified, specifically including: Determine the length and width of the region of interest based on the size of the region of interest of the optimal image; Based on the length and width of the region of interest, the optimal image is cropped using a calibrated camera to obtain an image of interest with features to be identified.

6. A visual intelligent adjustment system for a fully automatic wafer loading machine, using the visual intelligent adjustment method for a fully automatic wafer loading machine according to any one of claims 1 to 5, characterized in that: The visual intelligent adjustment system of the fully automatic loader includes: An image acquisition module is used to acquire an image of a feature to be identified in real time based on a camera in a fully automatic wafer mounter to obtain a real-time image; the feature to be identified is a wafer or a substrate; An optimal light source and optimal brightness determination module, connected to the image acquisition module, for determining the optimal light source and optimal brightness of the feature to be identified based on the multiple light sources in the full-automatic loader and the real-time image; A template region determination module, connected to the optimal light source and optimal brightness determination module, is used to obtain a feature image to be identified under the optimal light source and optimal brightness, obtain an optimal image, and determine a template region of the optimal image; An area of ​​interest determination module, connected to the template area determination module, and configured to determine the area of ​​interest of the optimal image based on the template area of ​​the optimal image; The adjustment module is connected to the region of interest determination module and is used to crop the optimal image based on the region of interest of the optimal image to obtain an image of interest of the feature to be identified; and adjust the camera based on the image of interest of the feature to be identified.

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