Ingot surface defect detection method and device and electronic equipment

Abstract

The invention relates to an ingot surface defect detection method and device and electronic equipment. The ingot surface defect detection method comprises the following steps: inputting surface data of a to-be-detected ingot into a first defect identification model to enable the first defect identification model to output a first defect identification result; when the first defect identification result indicates that the to-be-detected material ingot has the surface defect, inputting a part of surface data of the to-be-detected material ingot into a second defect identification model to enable the second defect identification model to output a second defect identification result; and generating quality grade data of the to-be-detected ingot according to the first defect identification result or the second defect identification result. The detection method is accurate in judgment.

Description

Technical Field

[0001] The present application relates to the field of semiconductor processing technology, and in particular to a method, device and electronic equipment for detecting surface defects of an ingot. Background Art

[0002] In the process of semiconductor ingot processing, defects are inevitable in every process. If defective products flow into subsequent processes, it will cause waste of production resources and costs. Therefore, timely detection of defective products is extremely important for reducing the cost of semiconductor processing.

[0003] In the prior art, there is a method in which a semiconductor ingot is judged as unqualified after a defect is detected on its surface, and the ingot is sent to an unqualified area. There is a misjudgment of the defect type that does not belong to the surface structure on the ingot, and manual re-judgment from the unqualified area is required, which has low judgment accuracy and low efficiency. Summary of the invention

[0004] The embodiments of the present application provide a method, device and electronic device for detecting surface defects of an ingot. The method is based on the detection of semiconductor ingots, and the detection process is simple, thereby avoiding misjudgment of the non-surface structural defect type of the ingot. The defect type can be accurately judged, ensuring the accuracy and efficiency of the ingot defect judgment, thereby at least partially solving the above-mentioned technical problems.

[0005] To achieve the above object, according to a first aspect of the present application, a method for detecting surface defects of an ingot is provided, comprising:

[0006] Inputting the surface data of the ingot to be tested into a first defect recognition model so that the first defect recognition model outputs a first defect recognition result;

[0007] When the first defect recognition result indicates that the ingot to be tested has a surface defect, inputting a portion of surface data of the ingot to be tested into a second defect recognition model so that the second defect recognition model outputs a second defect recognition result;

[0008] The quality grade data of the ingot to be tested is generated according to the first defect recognition result or the second defect recognition result.

[0009] Optionally, when the first defect recognition result indicates that the ingot to be measured has a surface defect, inputting a portion of surface data of the ingot to be measured into a second defect recognition model so that the second defect recognition model outputs a second defect recognition result, comprises:

[0010] When the first defect recognition result indicates that the ingot to be tested has a surface defect, the surface defect is selected and the type of the surface defect is indicated.

[0011] Optionally, the detection method further comprises:

[0012] Sorting instruction data for sorting the ingots to be tested are generated according to the quality grade data of the ingots to be tested.

[0013] Optionally, the detection method further comprises:

[0014] Acquire the original image of the ingot to be measured by an image collector; generate the surface data according to the original image of the ingot to be measured;

[0015] The surface data includes RGB images and three-dimensional point clouds.

[0016] Optionally, the acquiring the original image of the ingot to be measured by an image collector comprises:

[0017] Controlling the ingot to be tested to move along a preset trajectory;

[0018] Acquire an image point set of a plurality of position points on the first surface of the ingot to be measured along the preset trajectory;

[0019] Generate the original image according to the image point set;

[0020] Wherein, the first surface is arranged opposite to the image collector.

[0021] Optionally, controlling the ingot to be tested to move along a preset trajectory includes:

[0022] The ingot to be tested is controlled to move from a preset position along the preset track.

[0023] Optionally, the acquiring the original image of the ingot to be measured by an image collector further includes:

[0024] Acquire height data of the first surface; wherein the height data is height information of different positions on the first surface;

[0025] According to the height data, the vertical distance between the first surface and the image collector is adjusted during the movement of the ingot to be measured along the preset trajectory.

[0026] According to a second aspect of the present application, a method for detecting surface defects of an ingot is provided, which is implemented by an ingot surface defect detection system. The ingot surface defect detection system includes a controller, and the controller includes:

[0027] A first defect recognition module, used for acquiring surface data of the ingot to be measured and outputting a first defect recognition result according to the surface data of the ingot to be measured;

[0028] a second defect recognition module, configured to obtain a portion of surface data of the ingot to be measured when the first defect recognition result indicates that the ingot to be measured has a surface defect, and output a second defect recognition result according to the portion of surface data;

[0029] A generating module is used to generate quality grade data of the ingot to be tested according to the first defect recognition result or the second defect recognition result.

[0030] Optionally, the ingot surface defect detection system further comprises:

[0031] A moving part, in communication with the controller;

[0032] An image collector, connected to the controller for communication;

[0033] Wherein, the moving member is used to move the ingot to be measured along a preset track and to adjust the vertical distance between the first surface and the image collector during the movement of the ingot to be measured along the preset track;

[0034] The image collector is used to acquire a set of image points at a plurality of positions on a first surface of the ingot to be measured and generate a surface image during the movement of the ingot to be measured along the preset track.

[0035] Optionally, the image collector includes a visual sensor.

[0036] Optionally, the moving part is constructed as a three-dimensional moving linear module.

[0037] According to a third aspect of the present application, there is provided an ingot surface defect detection device, comprising:

[0038] a memory having a computer program stored thereon;

[0039] A processor is used to execute the computer program in the memory to implement the ingot surface defect detection method as described above.

[0040] According to a fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the ingot surface defect detection method as described above is implemented.

[0041] According to a fifth aspect of the present application, an electronic device is provided, characterized in that it includes the ingot surface defect detection device as described above.

[0042] The benefits of the present application are as follows: based on the detection of the ingot, the detection process is simple, and the misjudgment of the non-surface structural defect type of the ingot is avoided, and the accuracy and efficiency of the ingot defect judgment can be ensured according to the defect type.

[0043] More specifically, some embodiments of the present application may produce the following specific beneficial effects:

[0044] The ingot surface detection method provided in the embodiment of the present application inputs the surface data of the ingot to be tested into a first defect recognition model and enables the first defect recognition model to output a first defect recognition result. When the first defect recognition result indicates that the ingot to be tested has a surface defect, a part of the surface data of the ingot to be tested is input into a second defect recognition model, and the second defect recognition model is used to output a second defect recognition result. The quality grade data of the ingot to be tested is generated according to the first defect recognition result or the second defect recognition result, and the defect is judged again based on the first recognition result. The versatility of the deep learning algorithm and the effectiveness of the small-scale local point cloud processing algorithm are combined to improve the accuracy of defect detection, avoid misjudgment of the non-surface structural defect type of the ingot, and ensure the accuracy and efficiency of the ingot defect judgment according to the defect type.

[0045] The method for detecting surface defects of ingots provided in the embodiment of the present application obtains height data of the first surface and adjusts the vertical distance between the first surface and the image collector according to the height data while the ingot to be measured moves along a preset trajectory, so that the original image acquired by the image collector is clearer, and can adapt to surface defect detection of ingots of different models and specifications under different lighting conditions, thereby improving manufacturing efficiency and production capacity, and further improving the accuracy of defect identification.

[0046] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0048] In order to more completely understand the present application and its beneficial effects, the following description will be given in conjunction with the accompanying drawings, wherein the same figure numbers represent the same parts in the following description.

[0049] Figure 1 It is a flow chart of a method for detecting surface defects of an ingot provided in an embodiment of the present application;

[0050] Figure 2 is a block diagram of a material ingot surface defect detection system provided in an embodiment of the present application;

[0051] Figure 3It is a structural schematic diagram of a material ingot surface defect detection system provided in an embodiment of the present application;

[0052] Figure 4 It is a schematic diagram of a connection structure between a stage and a moving part provided in an embodiment of the present application;

[0053] Figure 5 It is a block diagram of a device for detecting surface defects of an ingot provided in an embodiment of the present application.

[0054] Description of reference numerals:

[0055] 200. Ingot surface defect detection system;

[0056] 210. Controller;

[0057] 211, first defect recognition module; 212, second defect recognition module; 213, generation module;

[0058] 220, moving part; 230, image acquisition device; 240, stage. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely 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. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0060] Reference Figure 3 As shown, for the convenience of introduction, the upper, lower, left, right, front and rear directions are adopted in the corresponding drawings to facilitate the introduction of the relative position relationship between the various parts in the present application, which should not be understood as a limitation on the absolute position.

[0061] Moreover, in the present application, the first direction corresponds to the front-to-back direction, the second direction corresponds to the left-to-right direction, and the third direction corresponds to the up-to-down direction; similarly, the first direction here indicates the front-to-back direction only for the convenience of introducing the specific embodiments of the present application, and there is no absolute corresponding relationship between the first direction and the front-to-back direction. Similarly, there is no absolute corresponding relationship between the second direction and the left-to-right direction, and between the third direction and the up-to-down direction.

[0062] The first direction, the second direction and the third direction of the present application are only for expressing relative positional relationships. They only indicate approximate directions rather than absolute geometric relationships.

[0063] In the process of semiconductor ingot processing, defects are inevitable in every process. If defective products flow into subsequent processes, it will cause waste of production resources and costs. Therefore, timely detection of defective products is extremely important for reducing the cost of semiconductor processing.

[0064] According to a first aspect of the present application, an embodiment of the present application provides a method for detecting surface defects of an ingot.

[0065] The surface defects in the present application are surface structural defects, such as point defects, line defects and surface defects on the surface of the ingot.

[0066] See also Figure 1 , Figure 1 : is a flow chart of a method for detecting surface defects of an ingot provided in an embodiment of the present application. The method for detecting surface defects of an ingot may include the following steps:

[0067] Step S130: inputting the surface data of the ingot to be tested into the first defect recognition model so that the first defect recognition model outputs a first defect recognition result;

[0068] Step S140: when the first defect recognition result indicates that the ingot to be tested has a surface defect, a portion of surface data of the ingot to be tested is input into a second defect recognition model so that the second defect recognition model outputs a second defect recognition result;

[0069] Step S150: generating quality grade data of the ingot to be tested according to the first defect recognition result or the second defect recognition result.

[0070] The first defect recognition result is obtained by the first defect recognition model receiving the surface data of the ingot to be measured and performing corresponding processing.

[0071] Both the first defect recognition model and the second defect recognition model are neural network models based on deep learning. The training process of the recognition model for ingot surface defects is prior art and will not be elaborated in this application.

[0072] It should be noted that in this application, the quality grade data is corresponded to the corresponding data of the surface defects of the ingot, and the ingots to be tested are divided into grades, which may include qualified and unqualified, or other grade classifications, such as S (perfect), A (some defects, but does not affect the next process), NG (unqualified, unusable scrap), and the specific settings are made according to actual needs, which will not be elaborated in this application.

[0073] By inputting the surface data of the ingot to be tested into a first defect recognition model and making the first defect recognition model output a first defect recognition result, when the first defect recognition result indicates that the ingot to be tested has surface defects, a part of the surface data of the ingot to be tested is input into a second defect recognition model, and the second defect recognition model is used to output a second defect recognition result, and the quality grade data of the ingot to be tested is generated according to the first defect recognition result or the second defect recognition result. The defect is further judged based on the first recognition result, and the universality of the deep learning algorithm and the effectiveness of the small-scale local point cloud processing algorithm are combined, thereby improving the accuracy of defect detection, avoiding misjudgment of the non-surface structural defect type of the ingot, and ensuring the accuracy and efficiency of the ingot defect judgment according to the defect type.

[0074] Specifically, when the first defect recognition result indicates that the ingot to be tested does not have surface defects, the ingot to be tested is judged to be qualified; when the first defect recognition result indicates that the ingot to be tested has surface defects, a part of the surface data of the ingot to be tested with defects is input into the second defect recognition model, and the second defect recognition model is used to output the second defect recognition result. If the second defect recognition result indicates that the ingot to be tested has surface defects, the ingot to be tested is judged to be unqualified; if the second defect recognition result indicates that the ingot to be tested does not have surface defects, the ingot to be tested is judged to be qualified.

[0075] In some embodiments, step 140 may include the following specific steps:

[0076] Step 141: When the first defect recognition result indicates that the ingot to be tested has a surface defect, a surface defect and a type of the indicated surface defect are selected.

[0077] Step 142: By selecting the surface defect and indicating the type of the surface defect, the second defect recognition model re-judges a portion of the surface data in a targeted manner to improve detection efficiency.

[0078] In some embodiments, the detection method further comprises:

[0079] Step 160: generating sorting instruction data for sorting the ingots to be tested according to the quality grade data of the ingots to be tested.

[0080] In some embodiments, reference Figure 1 Before step 130, the detection method further includes:

[0081] Step 110: acquiring an original image of the ingot to be tested through the image collector 230;

[0082] Step 120: generating surface data according to the original image of the ingot to be measured;

[0083] The surface data in step 120 includes an RGB image and a three-dimensional point cloud.

[0084] Due to the highly reflective and highly absorbent material properties of the ingot surface to be inspected, general sensors cannot effectively image its surface. Secondly, the surface defects of the ingot are usually in the form of tiny cracks, holes, etc., which are difficult to observe and easy to ignore. Based on the principle of white light confocal, the present application selects the image collector 230 as a visual sensor to achieve ultra-high resolution imaging and high-precision detection values ​​of the ingot surface to be inspected, and the measurement accuracy can reach the micron level.

[0085] At the same time, the encoder pulse signal triggers the visual sensor to collect images.

[0086] The visual sensor outputs a synthesized complete digital model of the ingot surface to be inspected, and the output formats include RGB images and three-dimensional point clouds.

[0087] In some embodiments, step 120 specifically includes:

[0088] Step 121: Control the ingot to be tested to move along a preset trajectory;

[0089] Step 122: Acquire an image point set of a plurality of position points on the first surface of the ingot to be measured along a preset trajectory;

[0090] Step 123: generating an original image according to the image point set;

[0091] The first surface is arranged opposite to the image collector 230 .

[0092] In the present application, since the size of the surface of the ingot to be detected is larger than the FOV of the visual sensor, and in order to be suitable for the detection of ingots of different models and specifications, the ingot to be detected is controlled to move along a preset trajectory in step 121. The preset trajectory is a surface formed along the first direction and the second direction, which can comprehensively obtain the image point set of several position points on the first surface of the ingot to be detected, and is suitable for the detection of ingots of different models and specifications.

[0093] In some embodiments, step 121 includes the following steps:

[0094] Control the ingot to be measured to move from a preset position along a preset trajectory.

[0095] By controlling the ingot to be measured to move from a preset position along a preset track, it is ensured that all-round surface data of the ingot to be measured is acquired to avoid missing data.

[0096] In some embodiments, step 120 further includes the following steps:

[0097] Get height data of the first surface.

[0098] The height data is the height information of different positions on the first surface;

[0099] According to the height data, the vertical distance between the first surface and the image collector 230 is adjusted during the movement of the ingot to be measured along the preset trajectory.

[0100] By adjusting the vertical distance between the first surface and the image collector 230 , it is ensured that the first surface of the ingot to be measured is located within the depth of field of the image collector 230 to achieve clear imaging.

[0101] According to a second aspect of the present application, a method for detecting surface defects of an ingot is provided, which is implemented by an ingot surface defect detection system 200, referring to Figures 2 to 4 The ingot surface defect detection system 200 includes a controller 210 , a moving part 220 , an image collector 230 and a stage 240 .

[0102] The stage 240 is used to carry the ingot to be tested, and the stage 240 is arranged on the moving part 220. A negative pressure device is arranged on the stage 240 to adsorb the ingot to be tested by negative pressure. In order to avoid the contact-type mechanical clamping device, which may cause secondary damage such as scratches and extrusion on the surface of the ingot when loading and unloading the ingot, a non-contact vacuum adsorption design is used in this application to fix the ingot on the stage 240 to avoid the influence of external vibration on the detection accuracy.

[0103] Among them, reference Figure 2 The controller 210 includes a first defect recognition module 211 , a second defect recognition module 212 and a generation module 213 .

[0104] The first defect recognition model in the first defect recognition module 211 is used to obtain surface data of the ingot to be tested and output a first defect recognition result according to the surface data of the ingot to be tested;

[0105] The second defect recognition module 212 is used for obtaining a portion of surface data of the ingot to be tested, and outputting a second defect recognition result according to the portion of surface data, when the first defect recognition result indicates that the ingot to be tested has a surface defect;

[0106] The generating module 213 is used to generate quality grade data of the ingot to be tested according to the first defect recognition result or the second defect recognition result.

[0107] Among them, the moving part 220 is used to move the ingot to be measured along a preset trajectory and adjust the vertical distance between the first surface and the image collector 230 during the movement of the ingot to be measured along the preset trajectory. The moving part 220 is constructed as a three-dimensional moving linear module, which is communicated with the controller 210. The three-dimensional motion platform composed of the three-dimensional moving linear module is connected to the stage 240. The three-dimensional moving linear module is used to move the ingot to be measured. The motion positioning accuracy reaches the micron level, realizing three-dimensional motion relative to the visual sensor, and completing image acquisition in a large range of three-dimensional space.

[0108] The image collector 230 is used to obtain a set of image points at a plurality of positions on the first surface of the ingot to be measured and generate a surface image when the ingot to be measured moves along a preset trajectory.

[0109] According to a third aspect of the present application, there is provided an ingot surface defect detection device, referring to Figure 5 As shown, the ingot surface defect detection device 300 may include a processing device 301 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. Various programs and data required for the operation of the ingot surface defect detection device 300 are also stored in the random access memory (RAM) 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0110] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication device 309 may allow the ingot surface defect detection device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The ingot surface defect detection device 300 having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0111] In particular, according to some embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present application are executed.

[0112] It should be noted that the computer-readable medium of some embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above, which is not specifically limited in the present application. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0113] In some embodiments of the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device. In some embodiments of the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0114] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as Hyper Text Transfer Protocol (HTTP), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), an internet (e.g., the Internet), and an advanced developer's hands-on conference (adho), as well as any currently known or future developed network.

[0115] The computer-readable medium may be included in the ingot surface defect detection device, or may exist independently without being assembled into the ingot surface defect detection device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the ingot surface defect detection device, the ingot surface defect detection device: inputs the surface data of the ingot to be tested into a first defect recognition model and enables the first defect recognition model to output a first defect recognition result. When the first defect recognition result indicates that the ingot to be tested has a surface defect, a part of the surface data of the ingot to be tested is input into a second defect recognition model, and a second defect recognition result is output using the second defect recognition model. The quality grade data of the ingot to be tested is generated according to the first defect recognition result or the second defect recognition result.

[0116] Computer program code for performing operations of some embodiments of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages. ― Such as Java, Smalltalk, C++, and also conventional procedural programming languages: such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network (including a local area network (LAN) or a wide area network (WAN)), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0117] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a portion of a code, which contains one or more executable instructions for implementing a specified logical function.

[0118] It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.

[0119] For example, two boxes shown in succession may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0120] The units described in some embodiments of the present application may be implemented by software or hardware. The units described may also be set in a processor, for example, may be described as: a processor includes an acquisition module and a drive module. The names of these units do not constitute a limitation on the units themselves in some cases, for example, the first defect recognition module may also be described as "used to obtain surface data of the ingot to be tested and output a first defect recognition result based on the surface data of the ingot to be tested".

[0121] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Parts (ASSP), System on Chips (SOC), Complex Programmable Logic Device (CPLD), etc.

[0122] According to a fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the ingot surface defect detection method as described above is implemented.

[0123] The non-transitory computer-readable storage medium has all the beneficial effects of the above-mentioned ingot surface defect detection method, which will not be described in detail in this application.

[0124] According to the fifth aspect of the present application, an embodiment of the present application further provides an electronic device, comprising the ingot surface defect detection device as described above. The electronic device has all the beneficial effects of the ingot surface defect detection method described above, which will not be described in detail in the present application.

[0125] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0126] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0127] The embodiments, implementation methods and related technical features of the present application can be combined and replaced with each other without conflict.

[0128] The above are only preferred embodiments of the present application and do not constitute any form of limitation to the present application. Although the descriptions of various embodiments in the embodiments of the present application have different focuses, for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application are still within the scope of the technical solution of the present application.

Claims

1. A method for detecting surface defects of an ingot, characterized in that: include: Inputting the surface data of the ingot to be tested into a first defect recognition model so that the first defect recognition model outputs a first defect recognition result; When the first defect recognition result indicates that the ingot to be tested has a surface defect, inputting a portion of surface data of the ingot to be tested into a second defect recognition model so that the second defect recognition model outputs a second defect recognition result; The quality grade data of the ingot to be tested is generated according to the first defect recognition result or the second defect recognition result.

2. The detection method according to claim 1, characterized in that: when the first defect recognition result indicates that the ingot to be tested has a surface defect, inputting a portion of the surface data of the ingot to be tested into a second defect recognition model so that the second defect recognition model outputs a second defect recognition result, include: When the first defect recognition result indicates that the ingot to be tested has a surface defect, the surface defect is selected and the type of the surface defect is indicated.

3. The detection method according to claim 1, characterized in that: The detection method further comprises: Sorting instruction data for sorting the ingots to be tested are generated according to the quality grade data of the ingots to be tested.

4. The detection method according to claim 1, characterized in that: The detection method further comprises: Acquire the original image of the ingot to be tested by an image collector; generating the surface data according to the original image of the ingot to be measured; The surface data includes RGB images and three-dimensional point clouds.

5. The detection method according to claim 4, characterized in that: The step of acquiring the original image of the ingot to be measured by an image collector comprises: Controlling the ingot to be tested to move along a preset trajectory; Acquire an image point set of a plurality of position points on the first surface of the ingot to be measured along the preset trajectory; Generate the original image according to the image point set; Wherein, the first surface is arranged opposite to the image collector.

6. The detection method according to claim 5, characterized in that: The controlling the ingot to be tested to move along a preset trajectory comprises: The ingot to be tested is controlled to move from a preset position along the preset track.

7. The detection method according to claim 5, characterized in that: The step of acquiring the original image of the ingot to be measured by an image collector further comprises: Acquire height data of the first surface; wherein the height data is height information of different positions on the first surface; According to the height data, the vertical distance between the first surface and the image collector is adjusted during the movement of the ingot to be measured along the preset trajectory.

8. A method for detecting surface defects of an ingot, implemented by an ingot surface defect detection system, characterized in that: The ingot surface defect detection system comprises a controller, and the controller comprises: A first defect recognition module, used for acquiring surface data of the ingot to be measured and outputting a first defect recognition result according to the surface data of the ingot to be measured; a second defect recognition module, configured to obtain a portion of surface data of the ingot to be measured when the first defect recognition result indicates that the ingot to be measured has a surface defect, and output a second defect recognition result according to the portion of surface data; A generating module is used to generate quality grade data of the ingot to be tested according to the first defect recognition result or the second defect recognition result.

9. The detection method according to claim 8, characterized in that: The ingot surface defect detection system also includes: A moving part, in communication with the controller; An image collector, connected to the controller for communication; Wherein, the moving member is used to move the ingot to be measured along a preset track and to adjust the vertical distance between the first surface and the image collector during the movement of the ingot to be measured along the preset track; The image collector is used to acquire a set of image points at a plurality of positions on a first surface of the ingot to be measured and generate a surface image during the movement of the ingot to be measured along the preset track.

10. The detection method according to claim 9, characterized in that: The image collector includes a visual sensor.

11. The detection method according to claim 9, characterized in that: The moving part is constructed as a three-dimensional moving linear module.

12. A device for detecting surface defects of an ingot, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the method for detecting surface defects of an ingot as claimed in any one of claims 1 to 11.

13. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting surface defects of an ingot according to any one of claims 1 to 11 is implemented.

14. An electronic device, characterized in that: It comprises the ingot surface defect detection device as claimed in claim 12.

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