An optical detection method, apparatus, medium, and computer program product
By using a closed-loop light intensity control mechanism, the light intensity of the optical inspection equipment is dynamically adjusted, which solves the problem that a fixed light intensity cannot adapt to the dynamic changes in the reflectivity and defect scattering efficiency of the object under test, thereby improving the detection accuracy and reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING OPTOKO MICROELECTRONICS TECH CO LTD
- Filing Date
- 2026-04-21
- Publication Date
- 2026-07-31
AI Technical Summary
In existing optical inspection technologies, fixed light intensity cannot adapt to the dynamic changes in reflectivity and defect scattering efficiency of different batches and materials of test objects, resulting in image oversaturation or signal submersion, low accuracy and poor reliability.
By constructing a closed-loop light intensity control mechanism, the light intensity of the device is dynamically adjusted based on the deviation between the image attribute features of the detected image and the preset attribute features until a match is achieved, thereby forming a target image for optical detection results.
It improves the accuracy and reliability of optical inspection, avoids the loss of defect details and missed detection, and adapts to the dynamic changes in the reflectivity and defect scattering efficiency of the test object.
Smart Images
Figure CN122487358A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of optical inspection technology, and in particular relates to an optical inspection method, device, medium and computer program product. Background Technology
[0002] Optical inspection technology is a key technology for quality inspection in the industrial field. It is widely used in defect detection scenarios for products such as wafers, glass substrates, and polished metal parts, and is divided into two categories: bright-field inspection and dark-field inspection. Both types of inspection technologies illuminate the object under test with light at a specific angle and image its surface, providing image evidence for subsequent defect identification and judgment.
[0003] Currently, optical inspection of the test object is generally performed using fixed-intensity illumination, i.e., irradiating the test object with a preset fixed light intensity. However, the reflectivity of test objects varies between different batches and materials, and the size, shape, and material of defects also differ, causing their scattering efficiency to exhibit dynamic changes. Fixed light intensity cannot adapt to these dynamic changes. For example, when the light intensity is high, the image of strongly scattering defects is prone to oversaturation, leading to the loss of defect details; when the light intensity is low, the signal of weakly scattering defects is overwhelmed by noise, resulting in missed defects. In other words, this fixed-intensity illumination method has low optical inspection accuracy and poor reliability. Summary of the Invention
[0004] This application provides an optical detection method, apparatus, medium, and computer program product that can improve the accuracy and reliability of optical detection.
[0005] A first aspect of this application provides an optical detection method, comprising: Based on the initial light intensity of the device, acquire detection images of the object to be tested; Identify candidate defect regions in the detected image; The regional attribute features of the candidate defect region and the global attribute features of the detected image are used as the image attribute features of the detected image. If the image attribute features do not match the preset attribute features of the test object, the device light intensity is adjusted according to the deviation between the image attribute features and the preset attribute features, and the detection image is acquired and the image attribute features are determined based on the adjusted device light intensity, until the image attribute features match the preset attribute features. The detected image when the image attribute features are matched with the preset attribute features is used as the target image, and the target image is used to determine the optical detection result of the object under test.
[0006] A second aspect of this application provides an optical detection device, comprising: The initial light intensity module is used to acquire a detection image of the object under test based on the initial light intensity of the device. The defect identification module is used to identify candidate defect regions in the detected image; The attribute feature module is used to take the regional attribute features of the candidate defect region and the global attribute features of the detection image as the image attribute features of the detection image. The light intensity adjustment module is used to adjust the light intensity of the device according to the deviation between the image attribute features and the preset attribute features when the image attribute features do not match the preset attribute features of the test object, and to re-acquire the detection image and determine the image attribute features based on the adjusted light intensity of the device until the image attribute features match the preset attribute features. The target detection module is used to take the detected image when the image attribute features are matched with the preset attribute features as the target image, and the target image is used to determine the optical detection result of the object to be tested.
[0007] A third aspect of the embodiments of this application provides an electronic device, the device comprising: a memory and a program or instructions stored in the memory and executable on a processor, wherein when the program or instructions are executed by the processor, they implement an optical detection method as provided in any of the embodiments of this application described above.
[0008] A fourth aspect of the embodiments of this application provides a readable storage medium on which a program or instructions are stored, and when the program or instructions are executed by a processor, they implement an optical detection method as provided in any of the embodiments of this application described above.
[0009] A fifth aspect of the embodiments of this application provides a computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform an optical detection method as provided in any of the embodiments of this application described above.
[0010] The technical solution provided in this application has at least the following beneficial effects: In an optical inspection method provided in this application, a detection image of the object under test is generated based on the initial light intensity of the device. Candidate defect regions in the detection image are identified, and the regional attribute features of the candidate defect regions and the global attribute features of the detection image are used as the image attribute features of the detection image. This avoids interference from irrelevant background information and accurately quantifies the compatibility between the device light intensity and the reflectivity and defect scattering efficiency of the object under test, providing initial data for subsequent optical inspection. When the image attribute features do not match the preset attribute features, it indicates that the current device light intensity cannot meet the optical inspection requirements. The device light intensity is adjusted based on the deviation between the two, and the detection image is re-acquired and the image attribute features are determined based on the adjusted device light intensity, forming a closed-loop light intensity adjustment mechanism. The light intensity is adjusted through the closed-loop light intensity adjustment mechanism until the image attribute features match the preset attribute features. The optical inspection result is determined based on the target image when the image attribute features match the preset attribute features. In other words, this application can dynamically adjust the light intensity based on the deviation between the image attribute features of the detection image and the preset attribute features, improving the accuracy and reliability of optical inspection. Attached Figure Description
[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 This is a schematic flowchart of an optical detection method provided in one embodiment of this application; Figure 2 This is a schematic diagram of the structure of an optical detection system provided in one embodiment of this application; Figure 3 This is a schematic diagram of a process for performing light intensity control based on global average grayscale, provided in one embodiment of this application; Figure 4 This is a schematic diagram of a process for controlling light intensity based on grayscale features according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an optical detection system including a beam-splitting element provided in one embodiment of this application; Figure 6 This is a schematic diagram of the structure of an optical detection device provided in one embodiment of this application; Figure 7 This is a schematic diagram of an optical inspection device provided in one embodiment of this application. Detailed Implementation
[0013] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.
[0014] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.
[0015] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.
[0016] First, the terms and concepts involved in one or more embodiments of this application will be explained.
[0017] Dark field inspection refers to an optical inspection technique that uses obliquely incident illumination light to illuminate the surface of the object under test, so as to form a high-contrast image of a dark background and bright defects.
[0018] Bright-field inspection refers to an optical inspection technique that uses normally incident illumination light to illuminate the surface of the object under test, thereby creating a high-contrast image of a bright background and the outline of defects.
[0019] Reflectivity is a physical parameter that measures the ability of a surface to reflect incident light.
[0020] Scattering efficiency is a parameter that refers to the ability of surface defects of a test object to scatter incident light.
[0021] Image oversaturation refers to an image distortion phenomenon where, due to excessively strong illumination, the grayscale value of the defect area in the detected image exceeds the dynamic range of the image sensor, and the pixel brightness reaches its maximum value and then stops changing, resulting in the loss of defect details.
[0022] Missed detection refers to a situation where, due to insufficient illumination intensity, the signal intensity of weakly scattering defects on the surface of the object under test is covered by noise, making it impossible to form identifiable defect features in the detection image, thus failing to identify the defect.
[0023] Image attribute features refer to parameters extracted from the detected image to quantify image quality and defect features, such as grayscale value and contrast.
[0024] Optical inspection technology is a key technology for quality inspection in the industrial field. It is widely used in defect detection scenarios for products such as wafers, glass substrates, and polished metal parts, and is divided into two categories: bright-field inspection and dark-field inspection. Both types of inspection technologies illuminate the object under test with light at a specific angle and image its surface, providing image evidence for subsequent defect identification and judgment.
[0025] Taking dark field detection as an example, dark field detection uses tilted illumination light to illuminate the object under test, causing the defects on the surface of the object to generate scattered light. This scattered light can be captured by a vertically arranged camera, while the smooth surface of the object under test will generate specular reflection light. This specular reflection light cannot enter the camera's field of view, forming a dark background, thus forming a high-contrast defect image, providing a basis for subsequent defect identification and judgment.
[0026] Currently, optical inspection of the test object is generally performed using fixed-intensity illumination, i.e., irradiating the test object with a preset fixed light intensity. However, the reflectivity of test objects varies between different batches and materials, and the size, shape, and material of defects also differ, causing their scattering efficiency to exhibit dynamic changes. Fixed light intensity cannot adapt to these dynamic changes. For example, when the light intensity is high, the image of strongly scattering defects is prone to oversaturation, leading to the loss of defect details; when the light intensity is low, the signal of weakly scattering defects is overwhelmed by noise, resulting in missed defects. In other words, this fixed-intensity illumination method has low optical inspection accuracy and poor reliability.
[0027] To address the aforementioned technical problems, this application provides an optical inspection method, apparatus, medium, and computer program product. In the optical inspection method provided in this application, firstly, the object under test is illuminated with an initial light intensity to acquire a corresponding inspection image. The regional attribute features of candidate defect areas in the inspection image and the global attribute features of the inspection image are used as the image attribute features of the inspection image. Secondly, the image attribute features are compared with preset attribute features. The light intensity of the device is adjusted according to the deviation between the two, and the process of image acquisition, attribute feature comparison, and light intensity adjustment is repeated until the image attribute features match the preset attribute features. Finally, based on the target image at the time of matching, defect identification and judgment of the object under test are completed, and the optical inspection result is obtained. That is, this application, by constructing a closed-loop light intensity control mechanism of light intensity output, image feedback, and dynamic adjustment of light intensity, achieves dynamic adaptation of light intensity with the reflectivity and defect scattering efficiency of the object under test, avoiding problems such as loss of defect details and missed detection caused by image oversaturation or weak signal, thus improving the accuracy and reliability of optical inspection.
[0028] For example, the optical inspection method provided in this application can be applied to the production line of a semiconductor manufacturing company for high-precision inspection of defects such as scratches, pits, and impurities on the wafer surface. In practical applications, this optical inspection method can be integrated as a standardized inspection process into the automated inspection equipment of the production line, and the inspection images, light intensity adjustment parameters, and optical inspection results generated during the inspection process can be stored in a data storage device. Operators can optimize the production process based on the inspection images and optical inspection results, such as analyzing defect distribution patterns to adjust semiconductor manufacturing process parameters and locating defect-prone stages.
[0029] It should be noted that the application scenarios described in the above embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will understand that with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems. The optical detection method provided by the embodiments of this application can be applied to various application scenarios that require adaptive adjustment of light intensity.
[0030] The optical detection method provided in the embodiments of this application is described below. In practical applications, the executing entity of the optical detection method in the embodiments of this application can be a terminal device, such as a desktop computer, a laptop computer, or a remote device similar to a server. Of course, the executing entity in the embodiments of this application can also be a software entity, such as a client or software program installed on a terminal device. The specific type of executing entity corresponding to the technical solution provided in the embodiments of this application is not strictly limited here, and can be flexibly selected according to the actual application scenario and actual needs.
[0031] The following describes specific embodiments of the optical detection method, apparatus, electronic device, storage medium, and computer program product provided in this application. First, an optical detection method is described.
[0032] Figure 1 This is a schematic flowchart of an optical detection method provided in an embodiment of this application. Figure 1 As shown, the method includes steps S100 to S104.
[0033] S100: Acquires detection images of the object under test based on the initial light intensity of the device.
[0034] In one or more embodiments of this application, in order to accurately extract image attribute features and compare them with preset attribute features in subsequent steps to determine whether the current light intensity is suitable for the detection-related characteristics such as the reflectivity and defect scattering efficiency of the object under test, this application needs to obtain a detection image reflecting the surface morphology of the object under test in this step.
[0035] Specifically, the object to be tested is placed on a moving platform, and by moving the platform, the object is adjusted to the center of the detection field of view. The illumination system is then activated, illuminating the object with a preset initial light intensity. Due to the scattering effect, the defect areas on the surface of the object generate scattered light. This scattered light is focused by the objective lens, transmitted through the tube lens, and then captured by an image detector (such as a CCD or CMOS camera) and converted into a digital signal, thereby obtaining a detection image containing the surface morphology of the object.
[0036] It should be noted that this application does not limit the specific type of the object under test, and can be set according to actual needs, such as semiconductor wafers, liquid crystal glass substrates, polished metal parts, precision optical components, and other components requiring surface defect detection. This application does not limit the specific magnitude of the initial light intensity, and can be set according to actual needs, such as determining the initial light intensity in advance based on the material of the object under test and historical detection data. This application does not limit the illumination method for optical detection, and can be set according to actual needs, such as dark field illumination, bright field illumination, etc. This application does not limit the specific structure of the optical detection equipment, and can be set according to actual needs. For example, the optical detection equipment may include components such as a light emitter, a light intensity modulator, a collimator, an objective lens, a tube lens, an image detector, and an image analysis unit. An aperture stop may also be set at the front of the objective lens to block specular reflection light. Of course, this application does not limit the specific types of each component, and can be set according to actual needs. For example, the emitter can be selected from lasers, white light sources, ring light sources, multi-zone ring light sources, etc., according to the detection accuracy and defect type; the collimator is used to collimate the divergent light emitted by the light source into parallel light, and a collimator component with angle adjustment function can be selected to adapt to the illumination angle requirements of different objects under test; in addition to graded attenuators, polarization attenuators or combinations thereof, the intensity modulator also adopts an electrically adjustable attenuator to improve the automation accuracy and response speed of intensity modulation; in the imaging system, different focal lengths of objective lenses and image detectors with different pixel specifications can be selected according to the detection field size and resolution requirements; the aperture can be selected as an adjustable aperture; the image analysis unit can select different hardware architectures according to real-time requirements, such as using a field-programmable gate array (FPGA) to achieve parallel processing in high-speed detection scenarios, and using a computer with image processing algorithm software and a digital signal processor (DSP) in complex image analysis scenarios.
[0037] Figure 2 This is a schematic diagram of the optical detection system provided in an embodiment of this application. Figure 2 As shown, the object to be tested 201 is placed on the moving platform 202. After the optical detection system is started, the object to be tested is moved to the target detection position. Then, the emitter 203 emits a light beam, and the light intensity is adjusted by the intensity regulator 204. After that, the divergent light is collimated into parallel light by the collimator 205, and then obliquely illuminates the surface of the object to be tested at a consistent angle. The imaging system includes an objective lens 206 perpendicular to the surface of the object to be tested, a tube lens 207, and an image detector 208. An aperture 209 can be set at the front end of the objective lens to block the specular reflection light. After the image detector acquires the detection image of the object to be tested, it transmits the detection image to the image analysis unit 210. The analysis result of the image analysis unit serves as the basis for the adjustment of the intensity regulator to adjust the light intensity of the device.
[0038] S101: Identify candidate defect regions in the detected image.
[0039] S102: The regional attribute features of the candidate defect region and the global attribute features of the detection image are used as the image attribute features of the detection image.
[0040] In one or more embodiments of this application, in order to compare the image attribute features of the detected image with the preset attribute features in subsequent steps, thereby providing a quantitative basis for light intensity adjustment and avoiding interference from irrelevant background information in light intensity judgment, and improving the pertinence and accuracy of subsequent light intensity adjustment, in steps S101 to S102, this application first needs to perform preliminary detection on the detection image of the object to be tested to locate candidate defect areas, and then extract image attribute features that can characterize image quality and defect features from the detection image by region and globally.
[0041] Specifically, the image analysis unit can receive the detection image acquired by the image detector, and then perform preprocessing operations (such as noise reduction, grayscale conversion, etc.) on the detection image, and then perform preliminary detection on the detection image to identify candidate defect regions, determine the regional attribute features of the candidate defect regions, and determine the global attribute features of the detection image; finally, the regional attribute features and the global attribute features are used as the image attribute features of the detection image.
[0042] It should be noted that this application does not limit the specific types of regional and global attribute features, which can be set according to actual needs. For example, image attribute features may include parameters that reflect light intensity adaptability, such as the global average gray value, the upper limit gray value of the candidate defect region, the gray standard deviation, and contrast. The global average gray value is related to the overall exposure state of the image and can characterize whether the current light intensity meets the basic imaging requirements; the gray standard deviation reflects the uniformity of the image brightness distribution; and the contrast characterizes the brightness difference between the defect and the background. This application does not limit the specific method for identifying candidate defect regions, which can be set according to actual needs, such as threshold segmentation algorithms.
[0043] S103: If the image attribute features do not match the preset attribute features of the object to be tested, adjust the light intensity of the device according to the deviation between the image attribute features and the preset attribute features, and acquire the detection image and determine the image attribute features based on the adjusted light intensity of the device until the image attribute features match the preset attribute features.
[0044] In one or more embodiments of this application, in order to generate a detection image based on the light intensity adapted to the characteristics of the object under test in subsequent steps, and then output the optical detection result of the object under test, it is necessary to avoid the loss of defect details or missed detection due to mismatch in light intensity. In this step, this application needs to dynamically adjust the light intensity through a closed-loop light intensity control mechanism of feature comparison, deviation calculation, light intensity adjustment, and iterative loop to find the light intensity that matches the image attribute features with the preset attribute features.
[0045] Specifically, the image attribute features extracted in S102 (such as global average grayscale value, current contrast, etc.) are compared with preset attribute features (such as preset grayscale range, reference contrast, etc.). If any image attribute feature does not match the preset attribute feature, the degree of deviation between the two is calculated. A light intensity adjustment strategy is determined based on the direction and magnitude of the deviation. For example, if the global average grayscale value is greater than the preset grayscale range, an adjustment command to reduce the light intensity is generated, with the adjustment magnitude positively correlated with the deviation magnitude. If the global average grayscale value is less than the preset grayscale range, an adjustment command to increase the light intensity is generated, with the adjustment magnitude positively correlated with the deviation magnitude. After the light intensity adjustment command is sent to the light intensity regulator, it adjusts the intensity of the light source. Then, the process returns to step S100, where the detection image is regenerated with the adjusted initial light intensity, and the attribute feature extraction and comparison adjustment process of step S101 are repeated until the image attribute features completely match the preset attribute features, at which point the closed-loop control process terminates.
[0046] It should be noted that this application does not limit the matching criteria, which can be set according to actual needs. For example, a match is determined if the deviation between the current contrast and the reference contrast is within a preset contrast deviation. Of course, this application does not limit the specific method of light intensity adjustment. For example, a gradient adjustment method of coarse adjustment followed by fine adjustment can be used to improve adjustment efficiency and ensure the accuracy of light intensity; light intensity can be adjusted according to a preset scaling ratio; light intensity can be adjusted according to a preset adjustment step size (such as 1% of the maximum light power); this application can also set an upper limit on the number of iterations to avoid loop deadlock in the closed-loop light intensity control process.
[0047] S104: The detected image when the image attribute features are matched with the preset attribute features is used as the target image, and the target image is used to determine the optical detection result of the object to be tested.
[0048] In one or more embodiments of this application, in this step, the application needs to perform optical detection operations such as defect identification, localization, and classification based on the target image acquired under the appropriate light intensity, so as to obtain the optical detection results of the object under test.
[0049] Specifically, the image analysis unit calls a preset defect detection algorithm to process the target image that meets the attribute feature matching requirements. For example, this application can process the target image using a threshold segmentation algorithm to identify defect areas, and then perform morphological analysis on the defect areas (such as calculating parameters such as area, perimeter, and shape factor). The extracted defect feature parameters are compared with standard parameters in a preset defect feature library to determine whether there are defects on the surface of the object under test, the specific location, size, and type of defects (such as scratches, pits, impurities, etc.), and integrate this information to obtain the optical detection results of the object under test.
[0050] It should be noted that this application does not limit the specific algorithm for defect detection, which can be set according to actual needs, such as threshold segmentation algorithm, edge detection algorithm, or deep learning-based target detection algorithm; at the same time, the output format of optical inspection results includes, but is not limited to, visual inspection reports, data files, etc., which are convenient for operators to view, archive and optimize subsequent production processes.
[0051] In the aforementioned optical inspection method, this application employs a closed-loop light intensity control mechanism consisting of light intensity output, image acquisition, feature comparison, and light intensity adjustment. It uses image attribute features as the quantitative basis for light intensity adaptability, adjusts the light intensity according to feature deviations, and iterates cyclically until attribute feature matching is achieved. This adapts to application scenarios where the reflectivity of the test object varies and the defect scattering efficiency changes dynamically. It solves problems such as loss of defect details and missed detection under fixed light intensity mode, thereby improving the accuracy, reliability, and automation of optical inspection.
[0052] Figure 3 This is a schematic diagram illustrating the process of performing light intensity control based on global average grayscale, as provided in an embodiment of this application. Figure 3 As shown, the process of performing light intensity control based on global average grayscale includes steps S301 to S306.
[0053] S301: Start the optical inspection system and move the object to be tested to the target inspection position.
[0054] S302: Initialize the optical detection system by outputting a light beam of initial intensity through a light intensity modulator according to a preset attenuation value. This light beam is then collimated and irradiated onto the object under test.
[0055] S303: Acquires a detection image of the object under test through an image detector and transmits the detection image to the image analysis unit.
[0056] S304: Determine the average gray value of the detected image through the image analysis unit.
[0057] S305: If the average gray value does not match the preset gray range, the image analysis unit generates an adjustment command to adjust the light intensity of the device based on the comparison result; the light intensity is adjusted by executing the adjustment command through the light intensity modulator, and the process returns to step S303.
[0058] For example, the preset grayscale range is set to [100, 150]. If the average grayscale value is less than 100, an adjustment quality to increase the light intensity is generated; if the average grayscale value is greater than 150, an adjustment command to decrease the light intensity is generated; the adjustment command is transmitted to the light intensity modulator, and after adjusting the light intensity, the process returns to step S303.
[0059] S306: When the average gray value matches the preset gray range, the image analysis unit determines the state maintenance command to maintain the current light intensity; the state maintenance command is transmitted to the light intensity modulator to perform subsequent optical detection based on the current light intensity.
[0060] Using the previous example, if the average gray value falls within the preset gray value range, the current light intensity will be maintained.
[0061] In step S102, in order to ensure the imaging quality of the candidate defect region while taking into account the overall imaging quality of the detected image, in one or more embodiments of this application, the regional attribute features include at least the upper limit gray value of the candidate defect region, and the global attribute features include at least the average gray value of the detected image. Based on this, the determination process of step S102 is as follows: This application can reduce the initial light intensity as an adjustment command when the upper limit grayscale value is greater than a preset grayscale threshold. When the upper limit grayscale value is not greater than the preset grayscale threshold and the average grayscale value is not within the preset grayscale range, the adjustment command is determined based on the deviation between the average grayscale value and the preset grayscale range. The light intensity of the device is adjusted according to the adjustment command.
[0062] Figure 4 This is a schematic diagram illustrating the process of light intensity control based on grayscale features, provided as an embodiment of this application. Figure 4 As shown, the process of controlling light intensity based on grayscale features includes steps S401 to S404.
[0063] S401: Determine the upper limit gray value of the candidate defect region and the average gray value of the detection image.
[0064] S402: If the upper limit grayscale value is greater than the preset grayscale threshold, the initial light intensity will be reduced as an adjustment command.
[0065] For example, the preset grayscale threshold can be set to 240. In order to prevent the candidate defect area from being oversaturated, if the upper limit grayscale value is greater than the preset grayscale threshold, an adjustment instruction to reduce the initial light intensity can be generated to ensure the integrity of the defect details.
[0066] S403: When the upper limit gray value is not greater than the preset gray value threshold and the average gray value is not within the preset gray value range, an adjustment instruction is determined based on the deviation between the average gray value and the preset gray value range.
[0067] S404: Adjust the light intensity of the device according to the adjustment instruction.
[0068] In this embodiment, the present application can optimize the imaging quality of other areas by adjusting the light intensity while ensuring the imaging quality of the candidate defect area, so as to take into account the overall imaging quality and ensure the stability of subsequent optical detection.
[0069] In step S100, in order to adapt the initial light intensity to the reflectivity of the object under test, reduce the number of iterations for subsequent light intensity adjustment, and improve detection efficiency, in one or more embodiments of this application, the initial light intensity can be calculated based on the calibration mapping relationship under a preset reflectivity, the reflectivity of the object under test, and preset attribute characteristics, as follows: This application can determine the ratio of preset reflectivity to the reflectivity of the object under test, and then determine the initial light intensity based on the calibration mapping relationship and ratio between light intensity and attribute characteristics under preset reflectivity and preset attribute characteristics.
[0070] It should be noted that this application does not limit the method for determining the calibration mapping relationship. It can be set according to actual needs, such as selecting a sample corresponding to a preset reflectivity for experimental calibration using an experimental calibration method; or using simulation modeling to simulate the imaging process of a sample with a preset reflectivity under different light intensities using optical simulation software. To accurately construct the calibration mapping relationship between light intensity and attribute features under a preset reflectivity and ensure the accuracy of the initial light intensity calculation, in one or more embodiments of this application, reference images of reference objects corresponding to preset reflectivities can be generated based on different light intensities. Then, the reference attribute features of each reference image are determined, and the calibration mapping relationship can be obtained by fitting each reference attribute feature with its corresponding light intensity. Of course, this application does not limit the specific type of attribute features; it can be set according to actual needs, such as global average gray value, gray standard deviation, contrast, etc. This application does not limit the specific format of the calibration mapping relationship; it can be set according to actual needs, such as lookup tables, functional equations, etc.
[0071] For example, with a preset reflectance of 60%, light beams of different intensities are output through a light intensity modulator to obtain reference images at each intensity. By determining the reference attribute characteristics of each reference image, such as the average gray value, a calibration mapping relationship can be fitted based on multiple data pairs of light intensities and reference attribute characteristics. The initial light intensity can then be determined based on the preset attribute characteristics under ideal conditions, the reflectance of the object under test, and this calibration mapping relationship.
[0072] Taking the calibration mapping relationship as a lookup table as an example, the lookup table can be used to query the matching light intensity that matches the preset attribute feature (average gray value is 150). If there is no data that completely matches the preset attribute feature in the lookup table, the matching light intensity can be determined by interpolation based on two light intensities that are close to the preset attribute feature (the light intensity is 40% when the gray value is 120; the light intensity is 60% when the gray value is 180). When the gray value is 150, the light intensity can be about 50%.
[0073] Assuming the reflectivity of the object to be tested is 80%, then based on the ratio of the preset reflectivity to the reflectivity of the object to be tested, and based on the correspondence between a light intensity of 50% and a preset reflectivity of 60%, it can be determined that the initial light intensity is 37.5% when the reflectivity is 80%, thus generating a detection image with an average grayscale value of 150.
[0074] The process for determining the initial light intensity described above is based on the following formula:
[0075] In formula (1), The initial light intensity; Preset reflectivity; The reflectance of the object being measured; The matching light intensity corresponding to the preset attribute features in the calibration mapping relationship.
[0076] Furthermore, to ensure the stability of optical detection and avoid image quality degradation caused by changes in the detection environment (such as light source attenuation or optical path contamination) or batch characteristics drift of the test object, in one or more embodiments of this application, a periodic monitoring and dynamic calibration of light intensity may be introduced, as follows: First, the light intensity at which the image attribute features match the preset attribute features is taken as the current light intensity. Second, a monitoring image is generated based on the current light intensity according to a preset period. Then, the monitoring attribute features of the monitoring image are determined. Finally, if the deviation between the monitoring attribute features and the preset attribute features is greater than a preset deviation threshold, the current light intensity is adjusted according to the magnitude relationship between the monitoring attribute features and the preset attribute features.
[0077] It should be noted that this application does not limit the acquisition stage of the monitoring images. For example, in wafer defect detection, after determining the appropriate light intensity based on the detection images of the same batch of wafers, monitoring images are generated according to the preset cycle during the optical detection stage of other wafers based on the current light intensity. This can capture light intensity deviations caused by factors such as light source power attenuation and lens contamination in the optical path. By dynamically calibrating the light intensity, the consistency of the detection standards for the same batch of test objects is ensured, avoiding batch missed detections or misjudgments due to cumulative errors. Of course, this application does not limit the specific type of monitoring attribute characteristics, which can be set according to actual needs, such as average gray value, reference contrast, etc. If it is an average gray value, a preset deviation threshold of 5 can be set to determine whether the current light intensity is suitable for the optical detection requirements. This application does not limit the specific size of the preset cycle, which can be set according to actual needs, such as acquiring monitoring images after detecting 10 fields of view, or every 5 seconds, to adapt to the optical detection frequency of different test objects and the drift characteristics of the optical detection system.
[0078] Furthermore, in order to address the problem of the inability to perceive and compensate for the attenuation of light flux in the imaging optical path, and to achieve precise control of light intensity across the entire link, in one or more embodiments of this application, the imaging optical path can be divided into a main optical path and a monitoring optical path using a beam splitter. The main optical path is used to generate the detection image, as detailed below: This application can monitor the current light flux of the monitoring optical path, and then adjust the light intensity of the device according to the deviation between the current light flux and the preset light flux when the current light flux does not match the preset light flux.
[0079] It should be noted that this application does not restrict the type and splitting ratio of the beam splitting element. Beam splitters, beam splitters, and other beam splitting elements can be selected according to the detection accuracy and imaging requirements. The splitting ratio can be flexibly set (e.g., 90% transmission, 10% reflection). The monitoring optical path does not need to measure the absolute luminous flux value. It only needs to continuously collect relative signals (e.g., voltage value or average gray value) that can represent the imaging luminous flux through the monitoring sensor to achieve effective monitoring of luminous flux changes. At the same time, this control process forms a new closed-loop control loop based on the internal monitoring of the imaging optical path. It has a fast response speed and can directly compensate for the problem of reduced transmittance caused by contamination or aging of optical components such as objective lenses and filters, ensuring the stability of the detection image quality.
[0080] Figure 5 This is a schematic diagram of the structure of an optical detection system including a beam-splitting element provided in an embodiment of this application. Figure 5 As shown, continue to use Figure 2In this optical detection system, a beam-splitting element 501 can be set between the objective lens 206 and the tube lens 207. This beam-splitting element can be a beam splitter and configured with a preset beam splitting ratio (e.g., 90% transmission, 10% reflection). The transmitted light passes through the beam splitter and continues to propagate along the original imaging optical path to generate a detection image, ensuring normal imaging of the main optical path. The reflected light is reflected by the beam splitter and guided to a monitoring sensor 502 (e.g., a photodiode, a CMOS sensor, etc.). The monitoring sensor monitors the relative light intensity change of the imaging optical path in real time, i.e., the degree of change in luminous flux. Of course, in this embodiment, it is not necessary to measure the absolute light intensity value or luminous flux; the monitoring function can be achieved simply by monitoring the signal value characterizing the luminous flux, such as voltage or average gray value. The output of the monitoring sensor is connected to the image analysis unit 210. The image analysis unit receives the signal fed back by the monitoring sensor in real time and compares it with the signal value corresponding to the preset light flux threshold. When the signal deviates from the threshold, a light intensity adjustment command is triggered to compensate for the light flux attenuation of the imaging optical path caused by factors such as optical element contamination and aging, so as to realize closed-loop control of the light intensity of the entire link.
[0081] In step S100, it is mentioned that the emitter can be a multi-segment ring light source. Therefore, in order to utilize the differentiated imaging effect of defects from different illumination angles and improve the detection rate of defects with directional characteristics (such as scratches and texture defects), in one or more embodiments of this application, this application can perform sequential partition lighting control on the multi-segment ring light source and perform fusion processing on the imaging results from different angles, as follows: Before step S100, this application can determine the initial light intensity of the device at each partition angle of the multi-partition ring light source. Then, the above-mentioned optical detection method can be used to adjust the light intensity of the device at each partition angle to obtain a suitable target image. The target images at each partition angle are fused to obtain a fused image, and optical detection is performed based on the fused image to obtain the optical detection result.
[0082] In this embodiment, the light intensity regulator can control the multi-segment ring light source to illuminate sequentially in a partitioned manner. For example, each 90° sector is illuminated in a clockwise direction. For each partition illuminated, the image acquisition module simultaneously acquires a detection image at the corresponding illumination angle. For the image acquired under each partition illumination, the closed-loop light intensity control process described above is executed separately to ensure that the image attribute features of the image acquired under each illumination angle meet the preset attribute features, thus ensuring the imaging quality of the image at a single illumination angle. After the illumination and imaging of all partitions are completed and the light intensity is optimized, the image analysis unit can perform an image fusion operation on N images (N is the number of partitions) at different angles to generate a fused image that integrates the defect feature information of each angle. Optical detection is then performed based on this fused image.
[0083] It should be noted that this application does not restrict the type of image fusion algorithm, and can be flexibly selected according to the type of defect. For example, for directional defects such as scratches, the maximum value fusion algorithm is used to enhance the contrast of the scratch edges. At the same time, the order of partition lighting and the number of partitions lit at one time can also be adjusted as needed. It can light up each partition individually or combine multiple adjacent partitions to adapt to the detection needs of different test objects.
[0084] Based on the above-described optical detection method, this application also provides a specific embodiment of an optical detection device.
[0085] like Figure 6 As shown in the figure, an optical inspection device 600 provided in this application embodiment includes an initial light intensity module 601, a defect identification module 602, an attribute feature module 603, a light intensity adjustment module 604, and a target detection module 605.
[0086] The initial light intensity module 601 is used to acquire a detection image of the object under test based on the initial light intensity of the device. The defect identification module 602 is used to identify candidate defect regions in the detection image; The attribute feature module 603 is used to take the regional attribute features of the candidate defect region and the global attribute features of the detection image as the image attribute features of the detection image. The light intensity adjustment module 604 is used to adjust the light intensity of the device according to the deviation between the image attribute features and the preset attribute features when the image attribute features do not match the preset attribute features of the test object, and to re-acquire the detection image and determine the image attribute features based on the adjusted light intensity of the device until the image attribute features match the preset attribute features. The target detection module 605 is used to take the detected image when the image attribute features are matched with the preset attribute features as the target image, and the target image is used to determine the optical detection result of the object to be tested.
[0087] In some embodiments, the light intensity adjustment module is specifically configured to: the regional attribute features include at least the upper limit gray value of the candidate defect region, and the global attribute features include at least the average gray value of the detected image; if the upper limit gray value is greater than a preset gray threshold, reduce the initial light intensity as an adjustment instruction; if the upper limit gray value is not greater than the preset gray threshold and the average gray value is not within a preset gray range, determine the adjustment instruction based on the deviation between the average gray value and the preset gray range; and adjust the initial light intensity according to the adjustment instruction.
[0088] In some embodiments, the initial light intensity module is specifically used to: determine the ratio of a preset reflectivity to the reflectivity of the object under test; and determine the initial light intensity based on the calibration mapping relationship between light intensity and attribute features at the preset reflectivity, the ratio, and the preset attribute features.
[0089] In some embodiments, the initial light intensity module described above is specifically used for: generating reference images of the reference objects corresponding to the preset reflectivity based on different light intensities; determining reference attribute features of each reference image; and fitting the calibration mapping relationship according to each reference attribute feature and its corresponding light intensity.
[0090] In some embodiments, the above-described apparatus further includes a monitoring module, specifically configured to: take the light intensity when the image attribute features match the preset attribute features as the current light intensity; generate a monitoring image based on the current light intensity according to a preset period; determine the monitoring attribute features of the monitoring image; and adjust the current light intensity according to the magnitude relationship between the monitoring attribute features and the preset attribute features when the deviation between the monitoring attribute features and the preset attribute features is greater than a preset deviation threshold.
[0091] In some embodiments, the monitoring module described above can also be used to: set a beam splitter in the imaging optical path of the detected image; divide the imaging optical path into a main optical path and a monitoring optical path by the beam splitter, wherein the main optical path is used to generate the detected image; monitor the current luminous flux of the monitoring optical path; and adjust the initial light intensity according to the deviation between the current luminous flux and the preset luminous flux when the current luminous flux does not match the preset luminous flux.
[0092] Based on an optical detection method, this application also provides a specific embodiment of an optical detection device.
[0093] Figure 7 A schematic diagram of the hardware structure of an optical detection device provided in an embodiment of this application is shown.
[0094] The optical inspection device may include a processor 701 and a memory 702 storing computer program instructions.
[0095] Specifically, the processor 701 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.
[0096] Memory 702 may include mass storage for data or instructions. For example, and not limitingly, memory 702 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 702 may include removable or non-removable (or fixed) media. Where appropriate, memory 702 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 702 is non-volatile solid-state memory.
[0097] The processor 701 implements any of the optical detection methods described in the above embodiments by reading and executing computer program instructions stored in the memory 702.
[0098] In one example, the electronic device may also include a communication interface 703 and a bus 710. Wherein, as... Figure 7 As shown, the processor 701, memory 702, and communication interface 703 are connected through bus 710 and complete communication with each other.
[0099] The communication interface 703 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.
[0100] Bus 710 includes hardware, software, or both, that couples the components of the electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 710 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.
[0101] Furthermore, in conjunction with the wafer morphology scanning signal processing method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the optical detection methods in the above embodiments.
[0102] In addition, in conjunction with the optical detection method in the above embodiments, this application embodiment can provide a computer program product for implementation. When the instructions in the computer program product are executed by the processor of an electronic device, the electronic device performs an optical detection method as provided in any aspect of the above embodiments of this application.
[0103] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.
[0104] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.
[0105] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.
[0106] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.
[0107] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.
Claims
1. An optical detection method, characterized in that, include: Based on the initial light intensity of the device, acquire detection images of the object to be tested; Identify candidate defect regions in the detected image; The regional attribute features of the candidate defect region and the global attribute features of the detected image are used as the image attribute features of the detected image. If the image attribute features do not match the preset attribute features of the test object, the device light intensity is adjusted according to the deviation between the image attribute features and the preset attribute features, and the detection image is acquired and the image attribute features are determined based on the adjusted device light intensity, until the image attribute features match the preset attribute features. The detected image when the image attribute features are matched with the preset attribute features is used as the target image, and the target image is used to determine the optical detection result of the object under test.
2. The method as described in claim 1, characterized in that, The regional attribute features include at least the upper limit gray value of the candidate defect region, and the global attribute features include at least the average gray value of the detected image; When the image attribute features do not match the preset attribute features of the object under test, the device light intensity is adjusted according to the deviation between the image attribute features and the preset attribute features, including: If the upper limit gray value is greater than the preset gray value threshold, the initial light intensity will be reduced as an adjustment instruction; If the upper limit grayscale value is not greater than the preset grayscale threshold and the average grayscale value is not within the preset grayscale range, the adjustment instruction is determined based on the deviation between the average grayscale value and the preset grayscale range. Adjust the initial light intensity according to the adjustment instruction.
3. The method as described in claim 1, characterized in that, Before generating a detection image of the object to be tested based on the initial light intensity, the method further includes: Determine the ratio of the preset reflectivity to the reflectivity of the object under test; The initial light intensity is determined based on the calibration mapping relationship between light intensity and attribute characteristics under the preset reflectivity, the ratio, and the preset attribute characteristics.
4. The method as described in claim 3, characterized in that, Before determining the initial light intensity based on the calibration mapping relationship between light intensity and attribute characteristics under the preset reflectivity, the ratio, and the preset attribute characteristics, the method further includes: Based on different light intensities, a reference image of the reference object corresponding to the preset reflectivity is generated; Determine the reference attribute features for each of the reference images; The calibration mapping relationship is obtained by fitting the reference attribute features and their corresponding light intensities.
5. The method as described in claim 1, characterized in that, When the image attribute features match the preset attribute features, after determining the optical detection result of the object to be tested based on the detected image, the method further includes: The light intensity at which the image attribute features match the preset attribute features is taken as the current light intensity; Based on the current light intensity, a monitoring image is generated according to a preset period. Determine the monitoring attribute characteristics of the monitored image; If the deviation between the monitored attribute feature and the preset attribute feature is greater than a preset deviation threshold, the current light intensity is adjusted according to the magnitude relationship between the monitored attribute feature and the preset attribute feature.
6. The method as described in claim 1, characterized in that, The method further includes: A beam-splitting element is provided in the imaging optical path of the detected image; The imaging optical path is divided into a main optical path and a monitoring optical path by the beam splitting element, and the main optical path is used to generate the detection image; Monitor the current optical flux of the monitored optical path; If the current luminous flux does not match the preset luminous flux, the light intensity of the device is adjusted according to the deviation between the current luminous flux and the preset luminous flux.
7. The method as described in any one of claims 1 to 6, characterized in that, Before acquiring a detection image of the object to be tested based on the initial light intensity of the device, the method further includes: For each zone angle of the multi-zone ring light source, determine the initial light intensity of the device at that zone angle; After using the detected image obtained by matching the image attribute features with the preset attribute features as the target image, the method further includes: The target images under each of the specified partition angles are fused to obtain a fused image; Optical detection is performed based on the fused image.
8. An optical detection device, characterized in that, include: The initial light intensity module is used to acquire a detection image of the object under test based on the initial light intensity of the device. The defect identification module is used to identify candidate defect regions in the detected image; The attribute feature module is used to take the regional attribute features of the candidate defect region and the global attribute features of the detection image as the image attribute features of the detection image. The light intensity adjustment module is used to adjust the light intensity of the device according to the deviation between the image attribute features and the preset attribute features when the image attribute features do not match the preset attribute features of the test object, and to re-acquire the detection image and determine the image attribute features based on the adjusted light intensity of the device until the image attribute features match the preset attribute features. The target detection module is used to take the detected image when the image attribute features are matched with the preset attribute features as the target image, and the target image is used to determine the optical detection result of the object to be tested.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the optical detection method as described in any one of claims 1-7.
10. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the optical detection method as described in any one of claims 1-7.