Defect detection method and defect detection equipment

Abstract

The invention provides a defect detection method and defect detection equipment, and the method comprises the steps: adjusting a first relative distance between a carrying device and an image collection device through a driving device in a direction parallel to a supporting surface, so as to enable the image collection device to be aligned with a to-be-detected sample; controlling the scanning device to scan the first surface of the to-be-detected sample to obtain identity information; controlling the image acquisition device to acquire an image of the second surface corresponding to the to-be-detected sample to obtain a to-be-detected image; performing defect identification on the to-be-detected image through the control device to obtain a defect identification result corresponding to the identity information, including preprocessing the to-be-detected image to obtain a chip area corresponding to the identity information; performing defect area identification on the chip area to obtain a defect identification result; and determining a target storage position according to the defect identification result, and storing the to-be-detected image to the target storage position. The method can improve the accuracy and speed of defect detection.

Description

Technical Field

[0001] The present invention relates to the field of defect detection technology, and in particular to a defect detection method and defect detection equipment. Background Art

[0002] During the preparation and transfer process, chips may be scratched or have foreign matter. To ensure the quality of the chip before delivery and to prevent scratches and foreign matter from being discovered after use, it is necessary to perform defect detection on the chip surface.

[0003] However, in the chip defect detection process, most people manually take photos of the chip surface to obtain chip images, and then use the chip images to identify defects. The defect identification effect is greatly affected by the operator, and the accuracy and speed of defect detection are low. Summary of the Invention

[0004] In view of the above, it is necessary to propose a defect detection method and defect detection equipment that can solve the problem of poor accuracy and speed of defect detection.

[0005] A first aspect of an embodiment of the present application provides a defect detection method, which is applied to a defect detection device, wherein the defect detection device includes a driving device, a loading device, an image acquisition device, a scanning device and a control device, wherein the loading device includes a supporting surface for supporting a sample to be detected, and the method includes: adjusting a first relative distance between the loading device and the image acquisition device along a direction parallel to the supporting surface by the driving device, so that the image acquisition device is aligned with the sample to be detected; controlling the scanning device to scan the first surface of the sample to be detected to obtain identity information; controlling the image acquisition device to acquire an image corresponding to the second surface of the sample to be detected to obtain an image to be detected; performing defect recognition on the image to be detected by the control device to obtain a defect recognition result corresponding to the identity information, including: preprocessing the image to be detected to obtain a chip area corresponding to the identity information; performing defect area recognition on the chip area to obtain a defect recognition result; determining a target storage location according to the defect recognition result, and storing the image to be detected in the target storage location.

[0006] Furthermore, in the above-mentioned defect detection method provided in an embodiment of the present application, the supporting surface includes a carrying component, and the carrying component is used to store a plurality of samples to be detected. Before adjusting the relative distance between the carrying device and the image acquisition device along a direction parallel to the supporting surface by the driving device to align the image acquisition device with the samples to be detected, the method also includes: determining the target position of the carrying component corresponding to each sample to be detected to obtain a plurality of target positions; determining the image acquisition sequence corresponding to the plurality of target positions; determining the target position of the image acquisition device corresponding to the field of view center on the supporting surface; and controlling the driving device to adjust the relative distance between the carrying device and the image acquisition device along a direction parallel to the supporting surface according to the image acquisition sequence to align the image acquisition device with the samples to be detected.

[0007] Furthermore, in the above-mentioned defect detection method provided in the embodiment of the present application, the preprocessing of the image to be detected to obtain the chip area corresponding to the sample to be detected includes: processing the image to be detected according to a preset image threshold algorithm to obtain a first image brightness value of each pixel in the image to be detected, and retaining pixels corresponding to the first image brightness value greater than or equal to the preset brightness threshold to obtain a first image to be detected; processing the first image to be detected according to a preset edge detection algorithm to obtain a second image to be detected; processing the second image to be detected according to a preset boundary search algorithm to obtain an initial boundary area; obtaining the sample attributes corresponding to the sample to be detected, and adjusting the initial boundary area according to the sample attributes to obtain a target boundary area; determining the chip area corresponding to the sample to be detected according to the target boundary area.

[0008] Furthermore, in the above-mentioned defect detection method provided in an embodiment of the present application, the adjusting the initial boundary area according to the sample attributes to obtain the target boundary area includes: parsing the sample attributes to obtain the first shape attribute and the first area attribute corresponding to the sample to be detected; adjusting the initial boundary area according to the first shape attribute and the first area attribute to obtain the target boundary area.

[0009] Furthermore, in the above-mentioned defect detection method provided in the embodiment of the present application, the chip area corresponding to the sample to be detected based on the target boundary area is determined, including: performing grayscale processing on the image within the target boundary area in the second image to be detected according to a preset brightness value to obtain a third image to be detected; processing the third image to be detected according to the preset image threshold algorithm to obtain a second image brightness value for each pixel in the third image to be detected, and adjusting the second image brightness value equal to the preset brightness value to a first target brightness value, and adjusting the second image brightness value greater than or less than the preset brightness value to a second target brightness value to obtain a fourth image to be detected; extracting the area corresponding to the first target brightness value in the fourth image to be detected as a chip mask; and determining the chip area corresponding to the sample to be detected based on the chip mask.

[0010] Furthermore, in the above-mentioned defect detection method provided in the embodiment of the present application, the defect area identification of the chip area to obtain a defect identification result includes: performing noise reduction processing on the chip area to obtain a target chip area; processing the target chip area according to a preset edge detection algorithm to obtain multiple border areas; obtaining the preset defect corresponding to the sample to be detected and the defect feature corresponding to the preset defect; if the defect area is identified from the multiple border areas according to the defect feature, it is determined that the defect identification result is that the sample to be detected has a defect; if the defect area is not identified from the multiple border areas according to the defect feature, it is determined that the defect identification result is that the sample to be detected does not have a defect.

[0011] Furthermore, in the above-mentioned defect detection method provided in an embodiment of the present application, the identifying of the defect area from the multiple border areas based on the preset defect characteristics includes: parsing the defect characteristics to obtain a first ratio interval corresponding to the preset defect; determining a second ratio of the length to the width corresponding to each border area; and identifying the border area corresponding to the second ratio belonging to the first ratio interval as the defect area.

[0012] Furthermore, in the above-mentioned defect detection method provided in the embodiment of the present application, the target storage location is determined according to the defect recognition result, and the image to be detected is stored in the target storage location, including: obtaining the target storage location corresponding to the defect recognition result according to the preset correspondence between the defect recognition result and the storage location; marking the defect area in the image to be detected to obtain the target image to be detected; and storing the target image to be detected in the target storage location.

[0013] The second aspect of the embodiment of the present application also provides a defect detection device, which includes a driving device, a carrier device, an image acquisition device, a scanning device and a control device. The carrier device includes a supporting surface for supporting a sample to be detected, wherein the driving device is used to drive the carrier device to move in a direction parallel to the supporting surface; the scanning device is suspended on the upper end surface of the carrier device, and is used to scan the first surface of the sample to be detected to obtain identity information; the image acquisition device is suspended on the upper end surface of the carrier device, and is used to acquire an image to be detected of the sample to be detected; and the control device is used to execute any of the defect detection methods described above.

[0014] Furthermore, in the above-mentioned defect detection equipment provided in the embodiment of the present application, the defect detection equipment also includes a light source control device, which is suspended on the upper end surface of the object-carrying device and is used to output light source when performing defect identification on the image to be detected.

[0015] A third aspect of the embodiments of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a controller, implements the steps of any one of the above-described defect detection methods.

[0016] The above-mentioned defect detection method provided in the embodiment of the present application first preprocesses the image to be detected to obtain the chip area corresponding to the sample to be detected, and then identifies the defect area corresponding to the sample to be detected based on the chip area, which can reduce the misrecognition rate and improve the accuracy and speed of chip defect detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings.

[0018] Figure 1 This is a structural diagram of a defect detection device provided in an embodiment of the present application;

[0019] Figure 2 This is a structural diagram of a load-bearing assembly provided in an embodiment of the present application;

[0020] Figure 3 This is another structural schematic diagram of a defect detection device provided in an embodiment of the present application;

[0021] Figure 4 This is a flow chart of a defect detection method provided in an embodiment of the present application;

[0022] Figure 5 This is a schematic diagram of a target location determination process provided by an embodiment of the present application;

[0023] Figure 6This is a schematic diagram of a chip area determination process provided by an embodiment of the present application;

[0024] Figure 7A is a schematic diagram of an image to be detected provided in an embodiment of the present application;

[0025] Figure 7B is a schematic diagram of the chip area provided in an embodiment of the present application;

[0026] Figure 8 This is a schematic diagram of a target boundary area determination process provided by an embodiment of the present application;

[0027] Figure 9 This is a schematic diagram of a chip area determination process provided by an embodiment of the present application;

[0028] Figure 10 This is a schematic diagram of a defect area identification process provided by an embodiment of the present application;

[0029] Figure 11A is a schematic diagram of the border area provided in an embodiment of the present application;

[0030] Figure 11B is a schematic diagram of a defect area provided in an embodiment of the present application;

[0031] Figure 12 This is a schematic diagram of a defect area selection process provided by an embodiment of the present application;

[0032] Figure 13 This is a schematic diagram of a storage process of an image to be detected provided in an embodiment of the present application.

[0033] Description of main component symbols

[0034]

[0035] DETAILED DESCRIPTION

[0036] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein may be combined with each other.

[0037] The following description sets forth many specific details to facilitate a full understanding of the present invention. The embodiments described are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0039] Combine Figure 1 The present invention provides a defect detection device. Figure 1 As shown, the defect detection device 1 includes a driving device 10, a loading device 20, an image acquisition device 30, a scanning device 40 and a control device 50 ( Figure 1 (not shown in the figure), the carrier 20 includes a supporting surface for supporting the sample to be detected, wherein the driving device 10 is used to drive the carrier 20 to move in a direction parallel to the supporting surface; the scanning device 40 is suspended on the lower end surface of the carrier 20, and is used to scan the first surface of the sample to be detected to obtain identity information; the image acquisition device 30 is suspended on the upper end surface of the carrier 20, and is used to acquire the image to be detected of the sample to be detected; the control device 50 is used to execute the defect detection method provided in the embodiment of the present application.

[0040] In one embodiment, the direction parallel to the supporting surface is recorded as the horizontal direction. Taking a two-dimensional coordinate system as an example, the horizontal direction may include the X-axis direction and the Y-axis direction. The driving device 10 may move along the X-axis direction and / or the Y-axis direction, and the object carrier 20 is adjusted to move in the horizontal direction accordingly, so that the image acquisition device 30 can capture images of different samples to be detected on the object carrier 20.

[0041] In one embodiment, a supporting surface corresponding to the object carrier 20 is provided with a carrying assembly 21, and the carrying assembly 21 is used to store a plurality of samples to be tested. Figure 2 , Figure 2 This is a schematic diagram of the structure of a carrier assembly 21 provided in an embodiment of the present application. The carrier assembly 21 is provided on a support surface. The carrier assembly 21 may include a carrier 211 and a carrier glass 212. The carrier 211 is provided with a plurality of glass slots, each of which is used to place a carrier glass 212; the carrier glass 212 is used to carry samples to be tested. In one embodiment, each carrier glass 212 is used to store four samples to be tested, and the number of carrier glass 212 is the same as the number of glass slots. Figure 2 As shown, there are three slide slots and three supporting slides 212. In one embodiment, multiple samples to be tested can be pre-attached to the supporting slides 212 and then placed in the slide slots. This embodiment of the present application only uses the support surface as an example of being able to store twelve samples to be tested.

[0042] In one embodiment, the first surface of the sample to be tested is provided with an identity code. The scanning device 40 is used to scan the first surface of each sample to be tested on the support surface to obtain the identity information of the sample to be tested, such as a chip QR code. The identity information may include a chip code, such as a chip QR code. The image acquisition device 30 is used to capture an image of the second surface of each sample to be tested at the target position on the support surface.

[0043] In one embodiment, the defect detection device 1 further includes a light source control device 60 , which is suspended on the upper end surface of the object carrier 20 and is used to output light when performing defect recognition on the image to be detected.

[0044] Figure 3 This is another structural diagram of a defect detection device provided in an embodiment of the present application. Figure 3 As shown, the defect detection device 1 may further include a storage device 70 and at least one communication bus 80. The at least one communication bus 80 is configured to enable communication between the storage device 70 and the controller. The storage device 70 stores a computer program; the controller is configured to implement the defect detection method when executing the computer program stored in the storage device 70.

[0045] Those skilled in the art should understand that Figure 3 The structure of the defect detection device 1 shown does not constitute a limitation of the embodiments of the present application. The defect detection device 1 may also include more or less other hardware or software than shown in the figure, or a different component arrangement.

[0046] In some embodiments, the defect detection device 1 can also be connected to a client device for communication, and the client device includes but is not limited to any electronic product that can interact with the user through a keyboard, mouse, remote control, touchpad or voice control device, such as a personal computer, tablet computer, smart phone, digital camera, etc.

[0047] In some embodiments, the defect detection device 1 may further include a battery module for powering various components. Preferably, the battery module may be logically connected to at least one controller via a power management device (not shown), thereby enabling power consumption functions through the power management device. The defect detection device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0048] Figure 4 This is a flow chart of a defect detection method provided in an embodiment of the present application, which is applied to a defect detection device. Figure 4 As shown, the following steps are included:

[0049] S11 , adjusting a first relative distance between the object carrying device and the image acquisition device along a direction parallel to the supporting surface by the driving device, so that the image acquisition device is aligned with the sample to be detected.

[0050] In one embodiment, before performing defect detection on a sample to be inspected, the driving device, the loading device, the image acquisition device, and the scanning device of the defect detection equipment are all in an initial state. The initial state of each of the above devices can be set according to actual needs. For example, taking the initial states of the loading device, the image acquisition device, and the scanning device as an example, in the initial state, the scanning device can scan the center position of the back side of the supporting surface corresponding to the loading device (in this case, the center of the field of view of the scanning device corresponds to the center position of the back side of the supporting surface), and the image acquisition device can scan the center position of the front side of the supporting surface corresponding to the loading device (in this case, the center of the field of view of the image acquisition device corresponds to the center position of the front side of the supporting surface), and the position of the center of the field of view of the image acquisition device on the supporting surface is used as the target position.

[0051] In one embodiment, when performing defect detection on a sample to be inspected, the driving device is adjusted along a direction parallel to the supporting surface (also simplified as the "horizontal direction" in this embodiment of the application) to drive the carrier device to move, and the first relative distance between the carrier device and the image acquisition device changes, so that the image acquisition device is aligned with the sample to be inspected, that is, the center of the field of view of the image acquisition device coincides with the sample to be inspected, so that the image acquisition device can capture a complete image of the sample to be inspected.

[0052] In one embodiment, the initial position of the sample to be detected on the supporting surface of the loading device is determined. In the horizontal direction, taking a two-dimensional coordinate system as an example, the initial position and the target position both include a first position parameter in the X-axis direction and a second position parameter in the Y-axis direction. For example, the target position is recorded as (0, 0) and the initial position is recorded as (X1, Y1). If the initial position (X1, Y1) is in the upper left corner of the target position (0, 0), the image acquisition device is aligned with the sample to be detected by adjusting the driving device to move X1 in the positive direction of the X-axis and Y1 in the negative direction of the Y-axis. The adjustment order of the driving device in the X-axis direction and the Y-axis direction can be set according to actual needs. For example, the driving device can be adjusted in the X-axis direction first and then in the Y-axis direction; or the driving device can be adjusted in the Y-axis direction first and then in the X-axis direction; or the driving device can be adjusted in the X-axis direction and the Y-axis direction at the same time, which is not limited here.

[0053] S12, controlling the scanning device to scan the first side of the sample to be detected to obtain identity information.

[0054] In one embodiment, the sample to be tested includes two sides, a first side and a second side. The first side may be the back side of the sample to be tested, and the second side may be the front side of the sample to be tested. An identity code, such as a chip QR code, is provided on the first side. A scanning device scans the first side of each sample to be tested on the support surface to obtain identity information of the sample to be tested, which may include the chip code.

[0055] In one embodiment, the second relative distance between the scanning device and the loading device can be fixed or variable. For example, in the case where the second relative distance between the scanning device and the loading device is variable, the scanning device can move in a direction perpendicular to the support surface, such that the scanning device approaches or moves away from the loading device. The direction perpendicular to the support surface is referred to as the vertical direction. By adjusting the second relative distance between the scanning device and the loading device, the embodiment of the present application enables the scanning device to quickly and accurately determine the identity information of the sample to be inspected, thereby improving the efficiency and accuracy of defect detection.

[0056] S13, controlling the image acquisition device to acquire an image corresponding to the second surface of the sample to be detected to obtain an image to be detected.

[0057] In one embodiment, the number of samples to be tested loaded on the support surface may be one or more. When the number of samples to be tested loaded on the support surface is one, the first relative distance between the loading device and the image acquisition device can be adjusted along a direction parallel to the support surface by controlling the driving device, and the image acquisition device can be aligned with the target position of the sample to be tested, thereby achieving defect detection of the sample to be tested. When the number of samples to be tested loaded on the support surface is more than one, the first relative distance between the loading device and the image acquisition device can be adjusted along a direction parallel to the support surface by controlling the driving device, and the image acquisition device can be aligned with the target position of each sample to be tested, and then the image acquisition device can be called to capture the image to be tested corresponding to the second surface of each sample to be tested. In one embodiment, the order in which the driving device adjusts the first relative distance between the loading device and the image acquisition device can be pre-set according to actual needs and is not limited here.

[0058] S14, performing defect recognition on the image to be detected by the control device to obtain a defect recognition result corresponding to the identity information, including:

[0059] Step 1: pre-process the image to be detected to obtain the chip area corresponding to the identity information;

[0060] In one embodiment, the image to be inspected is transmitted to a control device, which then performs defect detection based on the image to be inspected. During image acquisition, the background area of the image to be inspected can reduce the accuracy of defect detection due to the influence of exposure. Therefore, the image to be inspected needs to be preprocessed to remove the background area and obtain the chip area of the image to be inspected.

[0061] Step 2: identifying defective areas in the chip area to obtain defect identification results;

[0062] In one embodiment, after determining the chip region, defect identification can be performed on the chip region based on pre-set defects (also referred to as "pre-set defects" in this embodiment of the application) and defect characteristics to obtain a defect identification result. The defect identification result may include whether the sample to be tested has a defect or whether the sample to be tested has no defect. The pre-set defect refers to a pre-set defect with a high incidence rate during the chip preparation and transfer process. For example, the pre-set defect may be a scratch. The defect characteristics may refer to characteristics such as the ratio of the defect length to width.

[0063] Step 3: Determine a target storage location based on the defect recognition result, and store the image to be detected in the target storage location.

[0064] In one embodiment, the number of target storage locations may be one or more. For example, if there are multiple target storage locations, they are a first storage location and a second storage location, where the first storage location is used to store images corresponding to defective samples to be inspected, and the second storage location is used to store images corresponding to non-defective samples to be inspected.

[0065] In one embodiment, a preset identifier is established based on the identity information of the sample to be detected, and each preset identifier is used to identify a unique sample to be detected. The preset identifier can be a digital identifier, an alphabetic identifier, or a text identifier. The image to be detected is stored in the target storage location according to the preset identifier. For example, the preset identifier is added to the image to be detected in the form of adding a label; for another example, the preset identifier is added to the storage name corresponding to the image to be detected, and the image to be detected is stored with the storage name. In other embodiments, the identity information can also be used as the storage name of the image to be detected, and the image to be detected is stored with the storage name. In the embodiment of the present application, by directly using the identity information as the storage name to store the target image to be detected with defects in the target storage location or establishing a preset identifier corresponding to the identity information, and storing the target image to be detected with defects in the target storage location according to the preset identifier, the image to be detected can be quickly queried from the storage location for easy tracing.

[0066] The above-mentioned defect detection method provided in the embodiment of the present application first pre-processes the image to be detected to obtain the chip area corresponding to the sample to be detected, and then identifies the defect area corresponding to the sample to be detected based on the chip area, which can reduce the false recognition rate and improve the accuracy and speed of chip defect detection; and the present application controls the scanning device to scan the first side of the sample to be detected to obtain identity information, and then stores the image to be detected in combination with the identity information, which is convenient for subsequent tracing of the image to be detected, and avoids the problem of poor recognition accuracy and speed caused by manual identification of the identity information of the sample to be detected, thereby further improving the speed and accuracy of chip defect detection.

[0067] In one embodiment, the support surface includes a carrying component, and the carrying component is used to store a plurality of samples to be tested. Figure 5 This is a flow chart of a target position determination process provided by an embodiment of the present application. The target position determination method is applied to defect detection equipment. Figure 5 As shown, the following steps are included:

[0068] S21, determining the initial position of the carrying component corresponding to each sample to be detected, and obtaining multiple initial positions.

[0069] In one embodiment, if Figure 2 As shown, three carrying glass slides are provided on the supporting surface, each carrying glass slide is used to store four samples to be tested, the position of each sample to be tested in the carrying glass slide is known, and the center position of each carrying glass slide is used as the initial position of the sample to be tested on the supporting surface to obtain multiple initial positions.

[0070] S22: Determine an image acquisition sequence corresponding to the multiple initial positions.

[0071] In one embodiment, the image acquisition sequence refers to the sequence in which the pre-set control drive device adjusts the first relative distance between the carrier device and the image acquisition device in a direction parallel to the supporting surface. The number of image acquisition sequences can be multiple, and a corresponding acquisition name is set for each image acquisition sequence, for example, acquisition name A, acquisition name B and acquisition name C, which is not limited here. By selecting the acquisition name, the image acquisition sequence corresponding to the multiple initial positions is determined based on the correspondence between the acquisition name and the image acquisition sequence. Exemplarily, a total of 12 samples to be tested are loaded in the carrier assembly of the carrier device, and the leftmost carrier glass is used as the first carrier glass, the middle carrier glass is used as the second carrier glass, and the rightmost carrier glass is used as the third carrier glass. The samples to be tested on the first slide are labeled as sample A1, sample A2, and sample A3, from top to bottom; the samples to be tested on the second slide are labeled as sample B1, sample B2, and sample B3, from top to bottom; and the samples to be tested on the third slide are labeled as sample C1, sample C2, and sample C3, from top to bottom. The image acquisition sequence may refer to the order in which the drive device is controlled to horizontally adjust the relative distance between the samples to be tested and the image acquisition device in the following order: A1, A2, A3, B1, B2, B3, C1, C2, and C3.

[0072] S23, determining a target position of the image acquisition device corresponding to the center of the field of view on the support surface.

[0073] In one embodiment, a projection area of the image acquisition device corresponding to the center of the field of view on the support surface is determined, and the center point of the projection area is used as the target position.

[0074] S24, controlling the driving device to adjust a first relative distance between the loading device and the image acquisition device in a direction parallel to the supporting surface according to the image acquisition sequence, and adjusting the sample to be detected from the initial position to the target position, so that the image acquisition device is aligned with the sample to be detected.

[0075] In one embodiment, continuing from the above embodiment, the image acquisition sequence may refer to the order of controlling the driving device to horizontally adjust the first relative distance between the loading device and the image acquisition device in the following order: A1, A2, A3, B1, B2, B3, C1, C2 and C3, that is, first aligning the sample A1 to be detected with the image acquisition device, and then acquiring the image to be detected corresponding to the sample A1 to be detected, and then performing defect detection on the image to be detected and storing the image to be detected; then, aligning the sample A2 to be detected with the image acquisition device, and then acquiring the image to be detected corresponding to the sample A2 to be detected, and then performing defect detection on the image to be detected and storing the image to be detected; and looping through the above steps until defect detection is completed on the sample C3 to be detected.

[0076] The embodiment of the present application pre-sets the image acquisition sequence of multiple samples to be inspected in the loading device, and performs defect detection on the multiple samples to be inspected according to the image acquisition sequence, thereby avoiding missing samples to be inspected and improving the efficiency of defect detection; and performing defect detection on the samples to be inspected according to the image acquisition sequence can improve the efficiency of defect detection.

[0077] In one embodiment, due to the influence of exposure level, the background area of the image to be inspected may reduce the accuracy of defect recognition. Therefore, the image to be inspected needs to be preprocessed to remove the background area in the image to be inspected and obtain the chip area of the image to be inspected. Figure 6 This is a schematic diagram of a chip area determination process provided by an embodiment of the present application. The chip area determination method is applied to defect detection equipment. Figure 6 As shown, the following steps are included:

[0078] S31, processing the image to be detected according to a preset image threshold algorithm to obtain a first image brightness value of each pixel in the image to be detected, and retaining pixels corresponding to the first image brightness value greater than or equal to the preset brightness threshold to obtain a first image to be detected.

[0079] In one embodiment, a preset image threshold algorithm is used to convert the image to be detected into a binary image to simplify the complexity of image analysis and processing. For example, a first image brightness value is obtained for each pixel in the image to be detected, and the first image brightness value is compared with a preset brightness threshold. Pixels with a brightness greater than or equal to the preset brightness threshold are retained, and pixels with a brightness less than the preset brightness threshold are removed, thereby removing most of the background area in the image to be detected. The preset brightness threshold can be set according to actual needs and is not limited here.

[0080] In one embodiment, the accuracy of a preset image threshold algorithm may be affected by changes in lighting conditions. To eliminate the effects of lighting variations, the image to be detected can be processed using image averaging. Image averaging is a simple image smoothing method whose basic concept is to replace the pixel value with the average of its surrounding pixels. This method can smooth the image and eliminate the effects of lighting variations on image thresholding.

[0081] S32: Process the first image to be detected according to a preset edge detection algorithm to obtain a second image to be detected.

[0082] In one embodiment, a preset edge detection algorithm is used to determine edge information corresponding to the first image to be detected. For example, the preset edge detection algorithm can be a Canny algorithm. The preset edge detection algorithm can include steps such as Gaussian filtering, pixel gradient calculation, non-maximum suppression, hysteresis threshold processing, and isolated weak edge suppression. First, the first image to be detected is smoothed by Gaussian filtering to eliminate noise; then, the gradient strength and direction of each pixel in the first image to be detected are calculated using methods such as the Sobel operator; then, non-maximum suppression is applied to the gradient strength to refine the edge and reduce the occurrence of false edges; then, hysteresis threshold processing is performed to connect the edges, connecting strong edges together to form a continuous edge; finally, the final edge is determined by suppressing isolated weak edges to obtain the second image to be detected.

[0083] In one embodiment, after obtaining the second image to be detected, the method further includes performing a dilation operation on the second image to be detected. The size of the structuring element corresponding to the dilation operation can be set according to actual needs, for example, 5*5 pixels. During the dilation operation, if two objects are closely spaced, the two objects are connected together, thereby enhancing image connectivity.

[0084] S33: Process the second image to be detected according to a preset boundary search algorithm to obtain an initial boundary area.

[0085] In one embodiment, a preset boundary search algorithm is used to find and mark the boundary region of an object in an image. For example, an edge point of the object is first found in the second image to be detected, and then its neighboring pixels are searched in a specific order (e.g., clockwise) to obtain the next edge point. In this way, a closed sequence of points can be generated, i.e., the initial boundary region of the sample to be detected.

[0086] S34 , obtaining sample attributes corresponding to the sample to be detected, and adjusting the initial boundary area according to the sample attributes to obtain a target boundary area.

[0087] In one embodiment, the initial boundary region is composed of a closed sequence of points. The initial boundary region cannot be accurately determined down to every pixel. Therefore, the initial boundary region needs to be adjusted based on the sample attributes of the sample to be detected to obtain a target boundary region, thereby improving chip region recognition accuracy. The sample attributes corresponding to the sample to be detected may include, but are not limited to, shape and area attributes. Based on these shape and area attributes, the initial boundary region is adjusted to obtain a target boundary region.

[0088] S35 , determining the chip area corresponding to the sample to be detected according to the target boundary area.

[0089] See also Figure 7A and Figure 7B , Figure 7A is a schematic diagram of an image to be detected provided in an embodiment of the present application, Figure 7B is a schematic diagram of the chip area provided in the embodiment of the present application, such as Figure 7B As shown, the gray area is the determined chip area, and the black area is the background part that has not been completely removed.

[0090] The embodiment of the present application obtains the chip area corresponding to the sample to be detected by preprocessing the image to be detected, which can avoid the influence of the exposure degree of the sample to be detected on the defect recognition result during the image acquisition process and improve the accuracy of defect recognition.

[0091] Figure 8 This is a flow chart of determining a target boundary area provided by an embodiment of the present application. The target boundary area determination method is applied to defect detection equipment. Figure 8 As shown, the following steps are included:

[0092] S41 , analyzing the sample attributes to obtain a first shape attribute and a first area attribute corresponding to the sample to be detected.

[0093] In one embodiment, the sample attributes may be pre-set, and the sample attributes include a first shape attribute and a first area attribute of the sample to be detected, wherein the first shape attribute has a corresponding shape keyword, and the first area attribute has a corresponding area keyword. By determining the shape keyword and the area keyword from the sample attributes, and based on the index relationship between the shape keyword and the first shape attribute, and the index relationship between the area keyword and the first area attribute, the first shape attribute and the first area attribute corresponding to the sample to be detected can be obtained.

[0094] S42: Adjust the initial boundary region according to the first shape attribute and the first area attribute to obtain the target boundary region.

[0095] In one embodiment, in the point sequence corresponding to the initial boundary area, there may be one or more missing pixels, or one or more pixels may be positionally offset. When the initial boundary area is adjusted according to the first shape attribute and the first area attribute, the missing pixels can be supplemented, and the positions of the pixels that are positionally offset can be adjusted to obtain the target boundary area.

[0096] The embodiment of the present application adjusts the initial boundary area to the target boundary area according to the shape attributes and area attributes of the sample to be detected, which can improve the accuracy of the target boundary area selection and thus improve the accuracy of defect recognition.

[0097] Figure 9 This is a schematic diagram of a chip area determination process provided by an embodiment of the present application. The chip area determination method is applied to defect detection equipment. Figure 9 As shown, the following steps are included:

[0098] S51 , performing grayscale processing on the image within the target boundary area in the second image to be detected according to a preset brightness value to obtain a third image to be detected.

[0099] In one embodiment, the preset brightness value may refer to a preset pixel value for distinguishing images within the target boundary area, for example, the preset brightness value may be 100. The image within the target boundary area in the second image to be detected is subjected to grayscale 100 processing to obtain a third image to be detected.

[0100] S52, process the third image to be detected according to the preset image threshold algorithm to obtain the second image brightness value of each pixel in the third image to be detected, and adjust the second image brightness value equal to the preset brightness value to the first target brightness value, and adjust the second image brightness value greater than or less than the preset brightness value to the second target brightness value to obtain the fourth image to be detected.

[0101] In one embodiment, a second image brightness value of each pixel in the third image to be detected is obtained, the second image brightness value is compared with a preset brightness value, second image brightness values equal to the preset brightness value are adjusted to the first target brightness value, and second image brightness values greater than or less than the preset brightness value are adjusted to the second target brightness value, thereby obtaining a fourth image to be detected. The first target brightness value may be 255, and the second target brightness value may be 0, without limitation herein.

[0102] S53: Extracting an area corresponding to the first target brightness value in the fourth image to be detected as a chip mask.

[0103] In one embodiment, for samples of the same type to be detected, their corresponding sample attributes (e.g., shape and area) are the same. The region corresponding to the first target brightness value in the fourth image to be detected is used as a chip mask. The chip region of samples of the same type to be detected can then be quickly determined based on the chip mask, thereby improving the speed of chip region determination.

[0104] S54 , determining the chip area corresponding to the sample to be detected according to the chip mask.

[0105] In one embodiment, in the fourth image to be inspected, the area where the chip mask is located is determined as the chip area of the sample to be inspected.

[0106] In an embodiment of the present application, grayscale processing is performed on the image within the target boundary area in the second image to be detected according to a preset brightness value to obtain a third image to be detected; the third image to be detected is processed according to a preset image threshold algorithm to obtain a fourth image to be detected; and the area corresponding to the first target brightness value in the fourth image to be detected is extracted as a chip mask. After that, the chip area of the same type of sample to be detected can be quickly determined according to the chip mask, thereby simplifying the chip area determination process and improving the chip area determination rate.

[0107] Figure 10 This is a schematic diagram of a defect area identification process provided by an embodiment of the present application, and the defect area identification method is applied to defect detection equipment. Figure 10 As shown, the following steps are included:

[0108] S61, performing noise reduction processing on the chip area to obtain a target chip area.

[0109] In one embodiment, since some black or white spots are unavoidable during the production and transportation of the sample to be tested, noise reduction processing can be performed on the image corresponding to the chip area to obtain the target chip area. The noise reduction processing method may include median filtering and Gaussian blur processing, which are not limited here.

[0110] S62: Process the target chip area according to a preset edge detection algorithm to obtain multiple frame areas.

[0111] In one embodiment, a preset edge detection algorithm is used to detect multiple border areas from the target chip area. The preset edge detection algorithm has been described in detail above and will not be repeated here. Figure 11A , Figure 11A is a schematic diagram of the border area provided in an embodiment of the present application, Figure 11A In the example, only one border area is used. In other embodiments, multiple border areas may exist.

[0112] S63, obtaining a preset defect corresponding to the sample to be detected and a defect feature corresponding to the preset defect.

[0113] In one embodiment, the predetermined defect refers to a predetermined defect with a high incidence rate during chip preparation and transfer, for example, the predetermined defect may be a scratch. The defect feature may refer to a feature such as the ratio of the length to the width of the defect.

[0114] S64: If a defective area is identified from the multiple frame areas according to the defect feature, determine that the defect identification result is that the sample to be inspected has a defect.

[0115] S65 , if the defect area is not identified from the multiple frame areas according to the defect feature, determine that the defect identification result is that the sample to be detected has no defect.

[0116] See also Figure 11B , Figure 11B is a schematic diagram of a defect area provided in an embodiment of the present application, Figure 11B The border area in meets the defect characteristics corresponding to the preset defect, so the border area is regarded as the defect area.

[0117] The embodiment of the present application identifies the defect area corresponding to the sample to be detected based on the chip area, which can reduce the misidentification rate and improve the accuracy and speed of chip defect detection.

[0118] Figure 12 This is a schematic diagram of a defect area selection process provided by an embodiment of the present application. The defect area selection method is applied to defect detection equipment. Figure 12 As shown, the following steps are included:

[0119] S71, analyzing the defect characteristics to obtain a first ratio interval corresponding to the preset defect.

[0120] In one embodiment, multiple preset defects are framed to obtain multiple defect frames, and the ratio of the length to the width of each defect frame is used as a first ratio to obtain multiple first ratios. A first ratio interval is determined based on the multiple first ratios. For example, the smallest first ratio and the largest first ratio are selected from the multiple first ratios, and the first ratio interval is determined based on the smallest first ratio and the largest first ratio. For another example, the smallest first ratio is selected from the multiple first ratios, and the first ratio interval is determined based on the smallest first ratio (for example, the interval greater than the smallest first ratio is used as the first ratio interval). The defect border refers to the bounding rectangle that contains the complete preset defect. First, determine the first coordinate point (for example, the first coordinate point is the leftmost point) and the second coordinate point (for example, the second coordinate point is the rightmost point) of the preset defect in the horizontal direction; then, determine the third coordinate point (for example, the third coordinate point is the highest point) and the fourth coordinate point (for example, the fourth coordinate point is the lowest point) of the preset defect in the vertical direction; then, determine the first side length of the defect border based on the first coordinate point and the second coordinate point, and determine the second side length of the defect border based on the third coordinate point and the fourth coordinate point; then, determine the first ratio of the defect border based on the first side length and the second side length. For different preset defects, the corresponding first ratios are all predetermined. In one embodiment, the first ratio may be 2.5, and the first ratio range may be greater than 2.5.

[0121] S72: Determine a second ratio of the length to the width of each border area.

[0122] S73: Identify a frame area corresponding to a second ratio belonging to the first ratio interval as the defect area.

[0123] In one embodiment, when the second ratio falls within the first ratio interval, the border region corresponding to the second ratio is determined to be a defective region; when the second ratio does not fall within the first ratio interval, the border region corresponding to the second ratio is determined to be a non-defective region. This embodiment of the present application identifies defects in multiple border regions within a chip region based on defect characteristics, thereby improving the accuracy and speed of defect identification.

[0124] Figure 13 This is a schematic diagram of a storage process of an image to be detected provided by an embodiment of the present application, and the storage method of the image to be detected is applied to a defect detection device. Figure 13 As shown, the following steps are included:

[0125] S81, obtaining a target storage location corresponding to the defect identification result according to a preset correspondence between the defect identification result and the storage location.

[0126] In one embodiment, there is a corresponding relationship between the defect identification result and the storage location. For example, if the defect identification result is that the sample to be detected has defects, the target storage location is the first storage location; if the defect identification result is that the sample to be detected does not have defects, the target storage location is the second storage location.

[0127] S82: Mark the defect area in the image to be inspected to obtain a target image to be inspected.

[0128] In one embodiment, the marking method may include but is not limited to highlighting.

[0129] S83, storing the target image to be detected in the target storage location.

[0130] In one embodiment, the target storage location is used to store the image to be inspected corresponding to the sample to be inspected with defects.

[0131] In one embodiment, the identity information of the sample to be tested is used as the storage name and the image to be tested is stored to facilitate quick tracing.

[0132] The embodiment of the present application directly stores defective target images to be inspected in a target storage location according to identity information, and can quickly query the images to be inspected from the storage location for easy tracing.

[0133] then Figure 3 Regarding the description of the defect detection device, a computer program is stored in the storage device 70. When the computer program is executed by at least one controller, all or part of the steps in the above-mentioned defect detection method are implemented. The storage device 70 includes a read-only memory device 70 (ROM), a programmable read-only memory device 70 (PROM), an erasable programmable read-only memory device 70 (EPROM), a one-time programmable read-only memory device 70 (OTPROM), an electrically erasable programmable read-only memory device 70 (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage device 70, a magnetic disk storage device 70, a magnetic tape storage device 70, or any other computer-readable medium capable of carrying or storing data.

[0134] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the defect detection device 1, etc.

[0135] In some embodiments, at least one controller is the control core (Control Unit) of the defect detection device 1, which uses various interfaces and lines to connect the various components of the entire defect detection device 1, and executes various functions and processes data of the defect detection device 1 by running or executing programs or modules stored in the storage device 70, and calling data stored in the storage device 70. For example, when at least one controller executes the computer program stored in the storage device 70, it implements all or part of the steps of the adjustment method of the defect detection device in the embodiment of the present application; or implements all or part of the functions of the device adjustment device. At least one controller can be composed of an integrated circuit, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors and a combination of various control chips.

[0136] The above-mentioned integrated unit implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module is stored in a storage medium and includes a number of instructions for causing a defect detection device or a controller (processor) to execute parts of the methods of various embodiments of the present application.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is only a logical function division, and other division methods may be used in actual implementation.

[0138] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, and may be located in one place or distributed across multiple network elements. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0139] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0140] It is obvious to those skilled in the art that the present application is not limited to the details of the above-mentioned exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other units or, and the singular does not exclude the plural. Multiple units or devices stated in the specification may also be implemented by one unit or device through software or hardware. Words such as first, second, etc. are used to indicate names and do not indicate any particular order.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A defect detection method, characterized in that: Applied to a defect detection device, the defect detection device includes a driving device, a loading device, an image acquisition device, a scanning device, and a control device, the loading device includes a supporting surface for supporting a sample to be detected, and the method includes: adjusting a first relative distance between the object carrying device and the image acquisition device along a direction parallel to the supporting surface by the driving device so that the image acquisition device is aligned with the sample to be detected; Controlling the scanning device to scan the first side of the sample to be detected to obtain identity information; Controlling the image acquisition device to acquire an image corresponding to the second surface of the sample to be detected to obtain an image to be detected; Performing defect recognition on the image to be detected by the control device to obtain a defect recognition result corresponding to the identity information includes: Preprocessing the image to be detected to obtain the chip area corresponding to the identity information; performing defect area identification on the chip area to obtain a defect identification result; A target storage location is determined according to the defect recognition result, and the image to be inspected is stored in the target storage location.

2. The defect detection method according to claim 1, wherein: The supporting surface includes a carrying assembly, the carrying assembly being used to store a plurality of samples to be detected. Before adjusting a first relative distance between the object carrying device and the image acquisition device in a direction parallel to the supporting surface by the driving device to align the image acquisition device with the samples to be detected, the method further includes: Determining the initial position of the carrier assembly corresponding to each sample to be detected, and obtaining a plurality of initial positions; Determining an image acquisition sequence corresponding to the multiple initial positions; Determine a target position of the image acquisition device corresponding to the center of the field of view on the support surface; According to the image acquisition sequence, the driving device is controlled to adjust the first relative distance between the loading device and the image acquisition device in a direction parallel to the supporting surface, and the sample to be detected is adjusted from the initial position to the target position, so that the image acquisition device is aligned with the sample to be detected.

3. The defect detection method according to claim 1, wherein: The pre-processing of the image to be detected to obtain the chip area corresponding to the identity information includes: Processing the image to be detected according to a preset image threshold algorithm to obtain a first image brightness value of each pixel in the image to be detected, and retaining pixels corresponding to first image brightness values greater than or equal to the preset brightness threshold to obtain a first image to be detected; Processing the first image to be detected according to a preset edge detection algorithm to obtain a second image to be detected; Processing the second image to be detected according to a preset boundary search algorithm to obtain an initial boundary area; Acquiring sample attributes corresponding to the sample to be detected, and adjusting the initial boundary area according to the sample attributes to obtain a target boundary area; The chip area corresponding to the sample to be detected is determined according to the target boundary area.

4. The defect detection method according to claim 3, wherein: The adjusting the initial boundary region according to the sample attributes to obtain a target boundary region includes: Analyzing the sample attributes to obtain a first shape attribute and a first area attribute corresponding to the sample to be detected; The initial boundary region is adjusted according to the first shape attribute and the first area attribute to obtain the target boundary region.

5. The defect detection method according to claim 3, wherein: The step of determining the chip area corresponding to the sample to be detected according to the target boundary area includes: Performing grayscale processing on the image within the target boundary area in the second image to be detected according to a preset brightness value to obtain a third image to be detected; Processing the third image to be detected according to the preset image threshold algorithm to obtain a second image brightness value for each pixel in the third image to be detected, and adjusting the second image brightness value equal to the preset brightness value to the first target brightness value, and adjusting the second image brightness value greater than or less than the preset brightness value to the second target brightness value to obtain a fourth image to be detected; Extracting an area corresponding to the first target brightness value in the fourth image to be detected as a chip mask; The chip area corresponding to the sample to be detected is determined according to the chip mask.

6. The defect detection method according to claim 1, wherein: The step of identifying a defective area on the chip area to obtain a defect identification result includes: performing noise reduction processing on the chip area to obtain a target chip area; Processing the target chip area according to a preset edge detection algorithm to obtain multiple border areas; Obtaining a preset defect corresponding to the sample to be detected and a defect feature corresponding to the preset defect; If the defect area is identified from the multiple frame areas according to the defect feature, determining that the defect identification result is that the sample to be inspected has a defect; If the defect area is not identified from the multiple frame areas according to the defect feature, it is determined that the defect identification result is that the sample to be inspected does not have a defect.

7. The defect detection method according to claim 6, wherein: The identifying a defect area from the plurality of frame areas according to the preset defect feature includes: Analyzing the defect characteristics to obtain a first ratio interval corresponding to the preset defect; Determine a second ratio of the length to the width of each border area; A frame area corresponding to a second ratio belonging to the first ratio interval is identified as the defect area.

8. The defect detection method according to claim 1, wherein: The step of determining a target storage location according to the defect recognition result and storing the image to be detected in the target storage location includes: According to the preset correspondence between the defect identification result and the storage location, a target storage location corresponding to the defect identification result is obtained; Marking the defect area in the image to be inspected to obtain a target image to be inspected; The target image to be detected is stored in the target storage location.

9. A defect detection device, characterized in that: The defect detection device includes a driving device, a loading device, an image acquisition device, a scanning device and a control device. The loading device includes a supporting surface for supporting a sample to be detected, wherein: The driving device is used to drive the object carrying device to move in a direction parallel to the supporting surface; The scanning device is suspended on the upper end surface of the object-carrying device and is used to scan the first surface of the sample to be tested to obtain identity information; The image acquisition device is suspended on the upper end surface of the object carrying device and is used to acquire the image to be detected of the sample to be detected; The control device is used to execute the defect detection method according to any one of claims 1 to 8.

10. The defect detection device according to claim 9, wherein: The defect detection equipment further includes a light source control device, which is suspended on the upper end surface of the object-carrying device and is used to output light when performing defect recognition on the image to be detected.

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