277 results about "Defect region" patented technology

The invention provides a regeneration wafer defect detection method and system based on point cloud, and belongs to the technical field of semiconductor manufacturing detection, and the method comprises the steps: carrying out the registration and fusion processing of the point cloud data of a target wafer, and obtaining the target point cloud data; performing plane fitting on the target point cloud data to determine a reference plane; performing feature extraction on the target point cloud data relative to the reference plane to obtain feature data; positioning a defect area based on the feature data, and calculating geometric parameters of the defect area; correcting the geometric parameters by using a preset error compensation model to obtain target geometric parameters; inputting the position of the defect area, the target geometric parameter and the feature data of the corresponding defect area into a defect classification model to obtain a defect type of the defect area; and generating a defect detection result of the target wafer based on the defect types, the positions, the target geometric parameters and the feature data of all the defect areas of the target wafer. According to the invention, the efficiency and reliability of regenerated wafer defect detection are improved.

The invention provides a wafer defect detection method and system and electronic equipment, and relates to the technical field of machine vision, and the method fully utilizes the transformation relation between an imaging unit and an objective table in the wafer defect detection process to precisely splice strip images, thereby obtaining a high-precision wafer image, and improving the wafer detection precision. Meanwhile, an improved VAE model can be adopted to accurately obtain the defect area of the wafer to be detected from the brightness, the contrast ratio and the structural difference, high-precision recognition can be carried out on the fine defects, and therefore the problem that in the prior art, the fine defect detection effect is poor is solved.

The invention provides a semiconductor chip defect detection positioning method and system, and relates to the technical field of semiconductor chip detection, and the method comprises the steps: obtaining an original image and electrical test data; forming a defect candidate area based on local density analysis; variational mode decomposition is carried out on the electrical data, and electrical characteristic abnormal points are marked; mapping the image features to an electrical characteristic space and calculating mutual information values to determine defect points; and calculating the distribution and path of a diffusion field and determining the severity of the defect. According to the invention, accurate positioning and severity evaluation of chip defects are realized, and the detection efficiency and accuracy are improved.

The invention discloses a chip surface defect detection method, electronic equipment and a readable medium. The method comprises the following steps: aligning an ROI (Region of Interest) of a reference image with a test image through affine transformation; obtaining a gray average value of the ROI of the reference image and the ROI of the aligned test image, and carrying out gray stretching normalization processing on the test image based on the gray average value; respectively carrying out mean filtering on the reference image and the test image after normalization processing, and carrying out differential operation to obtain a differential image; performing threshold-based binary segmentation on the difference image, extracting surface detection candidate areas of bright defects and dark defects, performing morphological corrosion operation on the surface detection candidate areas to suppress noise, and generating a final defect mask; and mapping a defect area in the final defect mask back to the original test image coordinate system through inverse transformation of affine transformation. According to the method, high-precision smudginess defect extraction of the IPM chip area under the background of complex brightness can be realized, and the method is suitable for various complex packaging working conditions.

The invention relates to a defect detection method and device for an optical element, computer equipment and a storage medium. The method comprises the following steps: acquiring a target image of an optical element; based on the trained defect detection model, performing defect detection on the target image to obtain a defect identification frame corresponding to at least one detection target and a corresponding defect type label; the defect detection model is obtained based on deep learning model training; and carrying out fusion processing on the defect identification frames of the labels with the same defect type to obtain a defect area in the optical element. By adopting the method, adjacent or overlapped same-class defect areas can be effectively merged, so that a complete defect contour and a complete distribution range can be accurately restored.

The invention relates to a wafer defect detection method and system based on image neighborhood analysis, and the method comprises the steps: carrying out the image collection of a to-be-detected wafer, and obtaining a digital image containing a complete wafer back surface; performing traversal detection on each wafer back pixel point in the digital image; in the traversal detection process, through a dynamically adjusted single-size neighborhood window and / or a fixed multi-size neighborhood window, carrying out abnormity determination on the currently traversed wafer back pixel points until determination of all pixel points in the digital image is completed, and obtaining all abnormal pixel points; and carrying out clustering processing on the abnormal pixel points to obtain candidate defect areas in the digital image. Through the wafer defect detection method and device, a dynamic detection strategy and a fusion detection strategy are realized, the misjudgment risk of a global threshold is effectively overcome, the consistency of real defect detection is ensured, the defect detection precision of the wafer is improved, and the problem of how to improve the defect detection precision of the wafer is solved.

The invention discloses a storage chip defect detection method and system based on deep learning, and relates to the technical field of defect detection.In the system operation process, interference behaviors in the access process are collected, chip dynamic interference indexes are established, the response behaviors are integrated in a multi-dimensional parameter form, and a map structure is generated; coupling feature representation suitable for deep model input is formed through fusion, weak image features, region abnormal structures or control response spots in the atlas are recognized, the position, type and confidence mark of a defect region are output, abnormal records are generated in a coding mode, the abnormal records of multiple time points are constructed into a time sequence, and the time sequence is stored in a database; and performing evolution trend modeling based on indexes of the abnormal activity, the position change trend and the activation frequency, outputting a trend function for health level evaluation, receiving a health level result, matching the health level result with a control strategy table, and executing chip protection, working state adjustment or fault reporting operation.

The invention relates to the technical field of optical waveguide master mask manufacturing processes, and discloses a monitoring and correcting method and system for an optical waveguide master mask etching process, and the method comprises the steps: setting a plurality of monitoring regions and corresponding monitoring point positions, and collecting the real-time monitoring data of each monitoring point position; inputting real-time monitoring data into the etching analysis model to obtain a real-time etching coefficient of each monitoring point; judging whether correction is needed or not according to the real-time corrosion coefficient, and if yes, determining a plurality of local defect areas and generating a correction instruction; a correction instruction is issued, a feedback time node is set, a correction data packet of each local defect area is collected according to the feedback time node, a correction evaluation value is generated, whether the local defect area is corrected again or not is judged according to the correction evaluation value, and real-time monitoring and accurate correction of the etching process are effectively achieved. The problems that in a traditional etching process, defects are known after being perceived due to lack of real-time monitoring, and resources are wasted due to global reworking are solved.

The invention provides a strip steel defect detection method and device and electronic equipment, and the method comprises the steps: firstly detecting a defect region in a strip steel surface image, cutting out a defect region block, recording the position coordinate of the defect region block, setting a defect classification model, dividing a roller mark defect and a stain defect into the same target category, and carrying out the detection of the defect. And a defect classification model is adopted to determine a defect category corresponding to each defect area block, then position periodical detection is performed on the defect area blocks in the target category, the defect areas conforming to the position periodicity are determined as roll mark defects, and the defect areas not conforming to the position periodicity are determined as stain defects. According to the scheme, stain defects, roll mark defects and other various types of defects can be accurately distinguished in an image processing mode under the condition that stains on the surface of the strip steel are not wiped, so that the defects caused by the fact that the stains on the surface of the strip steel need to be removed firstly and then the defect types need to be detected in the prior art are overcome; and the detection of the roller mark defects is not interfered by other defect types, so that the detection accuracy is higher.

The invention provides an automatic detection method and system for the grinding machining surface of a precision workpiece, and belongs to the technical field of precision workpiece machining detection.The method comprises the steps that multi-mode image data of the grinding machining surface of a target precision workpiece and grinding parameters in the machining process are obtained; performing fusion preprocessing on the multi-modal image data to generate fused image features; feature extraction and cleaning are conducted on the grinding parameters, and grinding process features are generated; inputting the fused image features into a first-stage defect screening model to obtain a first detection result; when the existence confidence of any first defect is greater than a preset first confidence threshold, inputting the grinding process features into a defect mode diagnosis model to obtain a defect mode diagnosis result of the suspected defect area; determining a target defect detection model according to the defect mode diagnosis result; and inputting a part, corresponding to the suspected defect area, in the fused image feature into a target defect detection model to obtain surface defect geometric feature parameters of the suspected defect area.

The invention relates to the technical field of semiconductor detection, and discloses a method and a system for detecting chips on the surface of a semiconductor wafer. The method comprises the following steps: acquiring a high-resolution image of the surface of a wafer, and separating a defect candidate region set from a reference background region through noise filtering and contrast equalization processing; extracting defect areas to be identified one by one, and accessing the defect knowledge graph to obtain potential defect types; performing multi-feature fusion on the potential defect type and the reference background region, generating a defect semantic feature vector through a context sensing encoder, and analyzing the vector to judge the actual defect type; and processing all the candidate areas and then outputting a defect detection report. According to the method, the distinction degree of defects and backgrounds is enhanced, the defect judgment range is narrowed, similar defects are accurately recognized, missing detection and false detection are reduced, the detection efficiency and accuracy are improved, and reliable technical support is provided for wafer production quality control.

The invention discloses an unsupervised surface defect detection method for a piezoresistor. The unsupervised surface defect detection method specifically comprises the following steps: S1, acquiring a piezoresistor image and placing a defect-free and defective to-be-processed piezoresistor image; s2, generating a binary initial mask image, processing the binary initial mask image and the preprocessed image to obtain an effective candidate region, and performing spatial superposition on the DTD abnormal source image and the preprocessed image to obtain a pseudo abnormal image; and S3, constructing a reconstruction sub-network and a segmentation sub-network, performing reconstruction through the reconstruction sub-network to obtain a reconstructed image, and sending the reconstructed image and the preprocessed image into the segmentation sub-network to generate a defect segmentation image. According to the non-supervision surface defect detection method for the piezoresistor, the problem that in the prior art, a defect area and a normal area are difficult to accurately distinguish during defect segmentation, and consequently the segmentation result has a wrong segmentation phenomenon is solved.

The invention relates to the technical field of integrated circuit defect detection, and discloses an integrated circuit defect detection method and system using an optical means, and the method comprises the following steps: controlling the emission of incident light, and controlling the polarization state of the incident light, enabling the incident light to irradiate a detection area of the wafer with the integrated circuit in a preset polarization direction; returning light rays reflected or scattered from the detection area are collected, polarization state parameters of the returning light rays are calculated, and the polarization state parameters comprise the polarization degree and the polarization angle; and comparing the polarization state parameter with a reference polarization state parameter of a preset defect-free area so as to judge whether a defect exists on the wafer with the integrated circuit or not. According to the method, high-sensitivity and high-precision defect detection and classification are realized through polarization state analysis, and the depth of defect analysis is enhanced while the detection sensitivity and reliability are remarkably improved.

The invention relates to an AOI defect detection method and system based on image processing, in particular to the field of AOI defect detection, and can effectively overcome interference of complex texture backgrounds and accurately recognize tiny defects on a circuit board. A defect-free reference image is generated as a comparison reference, a defect area signal is enhanced in combination with a space attention mechanism, defect features of different sizes are captured by utilizing multi-scale feature fusion, and model adaptability and generalization ability are improved by means of transfer learning and meta-learning. And finally, the false alarm rate and the omission ratio are remarkably reduced while the detection precision is ensured.

The invention discloses a cooperative detection method and system for an intelligent industrial visual detection robot, and relates to the technical field of industrial visual detection. According to the method, detection items are divided into basic items, recheck items and recheck items, so that a multi-level item architecture of'basic guarantee + suspected verification + pressure balance 'is formed; judging suspected defect generation according to a preorder link basic result in a reinspection item, preferentially transmitting a whole item and secondarily preferentially transmitting a defect area, and triggering manual reinspection if the defect cannot be transmitted; reinspection items depend on a detection item library, are sequenced according to a preset detection link, quantify the detection pressure in combination with the number of items in a to-be-inspected link, and are dynamically allocated to a target link; in the multi-level project system, the basic project ensures that the core item is not missing, the re-inspection project focuses on the suspected risk point, and the re-inspection project supplements the cross-link detection blind area, so that the full-life-cycle detection requirement of the workpiece can be covered by cooperation of the basic project, the re-inspection project and the cross-link detection blind area

The invention belongs to the technical field of measuring equipment, and discloses a system and a method for detecting geometric dimensions and surface defects of tubes and bars, and the system comprises a laser diameter measuring instrument, a CCD (Charge Coupled Device) linear array camera and other core components. During detection, the laser diameter measuring instrument collects the circle center coordinate and the orthogonal diameter of the pipe bar, and the mean value of the double diameters is taken as the section diameter; introducing a correction parameter M based on the elliptical cross section model to calculate the real ovality; and the straightness of different positions of the pipe bar is calculated by combining an optimized three-point method. Meanwhile, a CCD camera is used for collecting circumferential images, defect areas are positioned through multi-step processing, features are extracted, and defects are classified through a cascade model. The system realizes synchronous online detection of geometric dimensions and surface defects of the pipes and the bars, breaks through the problems of single function, poor interference resistance, low efficiency and the like, and meets the strict standard of high-end manufacturing industry on the quality of the pipes and the bars.

The invention discloses an AI algorithm-based apparent defect detection method, system and device, and relates to the technical field of apparent defect detection. The appearance defect detection method based on the AI algorithm comprises the following steps: detecting defects of a high-reflection area; shadow area defect detection; and detecting defects of the sunken area. According to the method, the appearance defect area of the metal product to be detected is obtained through the AI visual sensor, the identification accuracy of the defect area in the high-reflection area is quantified, image contrast optimization judgment is carried out, then the area identification accuracy in the shadow area is quantified, and angle light source optimization judgment is carried out; and finally, the identification accuracy of the defect area in the concave area is quantified, and camera parameter optimization judgment is carried out, so that the identification accuracy of the specified appearance defect feature in the concave area corresponding to the high-transmittance and high-reflection metal is realized. The problem that in the prior art, high-transmittance and high-reflection metal is low in appearance defect feature recognition accuracy in a corresponding concave area in the appearance defect detection process is solved.

The invention provides an electrostatic chuck internal defect detection method and device, a terminal and a medium, the electrostatic chuck comprises a ceramic layer and a metal layer, the method comprises the following steps: carrying out point-by-point contact scanning on the surface of the ceramic layer through an eddy current probe, and collecting an impedance change value corresponding to each scanning point to obtain impedance change data; inputting impedance change data into the impedance thickness model, and calculating the ceramic layer thickness of each scanning point to obtain ceramic layer thickness data; and processing the thickness data of the ceramic layer, and determining an internal defect area of the electrostatic chuck. According to the method, eddy current is induced in the conductive material by utilizing the alternating magnetic field, efficient and contact type defect detection is realized by analyzing impedance change, online nondestructive detection of the internal defect and the film thickness of the electrostatic chuck is realized, and the bump defect is accurately identified.

The invention discloses a curved broken filament cluster-shaped region closing and defect area quantification method, which comprises the following steps of: firstly, performing image acquisition on a target region containing a curved broken filament defect to obtain an original image; preprocessing the original image to obtain a binary defect area image, and performing skeletonization processing to obtain a defect skeleton curve of a single pixel width; segmenting the skeleton curve and carrying out arc fitting to obtain a fitting circle and a starting point, an ending point and a midpoint of an arc section; judging whether the arc section exceeds a semicircle or not by constructing a triangle, and complementing the arc section exceeding the semicircle by using a fitting circle to form a closed area; and finally, combining all the closed areas to generate a defect mask, calculating an actual area and judging whether the defect is qualified or not according to a preset threshold value. According to the method, the bent broken filaments are subjected to reasonable ball-shaped region closing treatment, so that the quantized defect area is highly consistent with a manual visual inspection result.

The invention relates to the technical field of mold detection, and discloses a special working state inner wall detection device for a neutral borosilicate molded bottle mold, which comprises a workbench, an opening and closing mechanism is arranged on the middle side of the top of the workbench, a clamping mechanism is arranged on the top of the workbench, and a six-degree-of-freedom mechanical arm is mounted on the rear side of the top of the workbench. A ceramic probe is arranged on the outer side of the six-degree-of-freedom mechanical arm, and a base fixing seat is fixedly connected to the outer side of the ceramic probe. A real-time image differential analysis module is arranged, a blowing and powder spraying mechanism is used for carrying out pneumatic excitation on a suspected defect area, and a multispectral endoscope module is controlled to respectively collect optical images before and after excitation; by calculating the normalized difference measure of the two images and comparing the normalized difference measure with the preset physical displacement threshold value, the non-solid-phase attachments displaced due to pneumatic excitation and the solid-phase defects with fixed positions can be distinguished, and the technical problem that two abnormal areas with different properties are difficult to effectively classify in the prior art is solved.

The invention relates to the technical field of optical detection, in particular to an LED chip defect detection method and system, and the method comprises the following steps: obtaining an LED chip wiring area pattern layer, positioning interval change pixel point pairs, dividing image blocks, constructing a crack trend through a path consistent with the extension direction, supplementing a wiring boundary extension pattern layer, and obtaining an LED chip wiring area pattern layer. And extracting an electrode tail end brightness sequence, screening out disturbance lines, and concluding the disturbance lines as defect image areas according to a position range. According to the method, the crack trend is screened through the path extension direction, direction line segments are formed through communication, the coherence of structure extension information is enhanced, the wiring boundary is covered by a layer supplementing synchronous extension area, the discrimination precision of the spatial corresponding relation is enhanced, and the boundary brightness detection is combined with mirror image contrast to remove interference. A symmetric disturbance area is reserved as a judgment basis, a path extension trend and a wiring area direction are spatially matched, a crack propagation path identification chain is constructed, and defect area tracking integrity and stability are improved.

The invention provides a friction stir welding seam real-time defect detection method based on multistage context aggregation and knowledge distillation, and belongs to the technical field of welding seam defect detection.The method comprises the following steps that a friction stir welding seam image data set is constructed, and a constructed training set, a constructed verification set and a constructed test set are subjected to preprocessing operation; establishing a friction stir welding defect detection model based on ISIA-HKD, training the constructed defect detection model by using the pre-processed training set, checking a training result by using the pre-processed verification set to obtain an intermediate training model, evaluating the trained intermediate model, selecting the model with the best evaluation performance as a final model, and finally, carrying out defect detection on the friction stir welding defect. And deploying the final model. Local textures and global structural features of defects in a welding image are effectively captured through a multi-stage context aggregation module, noise is suppressed in combination with a gating mechanism, a defect area is highlighted, and the tiny defect detection capacity is remarkably improved.

The invention relates to a metal weld defect nondestructive identification method, device and equipment and a medium. The method comprises the following steps: acquiring original detection data of a to-be-detected metal welding seam; a welding seam area is extracted based on the original detection data, and welding seam area data are obtained; the original detection data comprises at least one of ultrasonic waveform data, ray image data and optical image data; inputting the weld joint area data into a pre-trained target detection model to obtain a candidate defect area; classifying defects in the candidate defect area to obtain defect instance information; the defects comprise at least one of cracks, air holes, slag inclusion, incomplete fusion and incomplete penetration; and calculating the physical size of the defect area based on the defect instance information to obtain defect detailed information. By adopting the method, the defect characteristics can be accurately quantified, and each defect area and various defects can be accurately divided.

The invention discloses a GPU chip phonon positioning vortex self-repairing cooling control method and system, and relates to the technical field of intelligent chip thermal management, and the method comprises the steps: analyzing a GPU chip design file, constructing a GPU chip digital twin, solving GPU chip deformation field distribution through a thermal-force coupling equation, and generating a dynamic deformation feature template library; applying an alternating magnetic field to induce quantum vortex in a magnetic fluid cooling environment, and performing feature face method dimension reduction calculation on the GPU chip surface image based on the dynamic deformation feature template library to generate a defect feature weight vector; and performing quantum resonance tuning detection according to the defect feature weight vector, and amplifying the phonon signal intensity of the defect area of the GPU chip through a phonon-magnetic field frequency matching equation to generate a submicron precision defect coordinate. According to the method, submicron precision defect coordinates are generated through quantum resonance tuning detection, and high-precision positioning of the local defect area of the GPU chip is achieved.

The invention discloses a defect detection method and system for a silicon wafer based on visual inspection, and relates to the technical field of visual inspection, the surface image of the silicon wafer is deeply excavated based on a multi-scale attention mechanism, and the primary defect content of the silicon wafer is subjected to multi-level fusion to output a plurality of primary defect areas; and each primary defect region and the corresponding associated region determine a final defect region in semantic dimension analysis, so that the accuracy of the final defect region is further improved. Performing multi-stage analysis on the plurality of silicon wafer defect characteristics along the gray gradient and the morphological parameters to determine the defect grade of each silicon wafer defect characteristic; according to the technical scheme, the plurality of defect combinations are determined according to the feature form of each silicon wafer defect feature and the corresponding defect level, the corresponding visual report is determined based on the identification of each defect combination, and the defect regulation and control instruction of the silicon wafer is determined along the multi-level iteration of the plurality of visual reports, so that the accuracy of the defect regulation and control instruction of the silicon wafer is improved.

The invention discloses a wafer defect detection method, a wafer defect detection device, wafer defect detection equipment and a storage medium, and relates to the technical field of semiconductor processing. The wafer defect image after depth-of-field fusion can clearly present details of all spatial positions, the edge breakage imaging effect at the wafer gap is improved, and the accuracy of the wafer defect detection result is ensured. The method comprises the following steps: acquiring a plurality of wafer defect images, wherein the wafer defect images are grayscale images acquired by imaging a wafer defect area for multiple times; carrying out definition positioning on each wafer defect image to obtain the definition intensity of different pixel areas in the wafer defect image; carrying out depth-of-field fusion on the pixel areas with the highest clear intensity in the different wafer defect images to obtain a fused image of the wafer defects; and detecting the fused image of the wafer defect to obtain a wafer defect detection result.

The invention relates to the field of polishing processes, in particular to a casting polishing track generation method and system.The casting polishing track generation method comprises the steps that a to-be-polished area is decomposed into a plurality of sub-polishing areas, area images of the sub-polishing areas are collected for pixel filtering to form corresponding filtering images, the filtering images are learned through a defect positioning model, and the casting polishing track is obtained. Identifying the number of defect areas to allocate grinding priorities for the sub-grinding areas, sequentially grinding the sub-grinding areas by using a plurality of grinding tool bits according to a fineness sequence, and when the wear degree data of the grinding tool bits reach a wear standard, carrying out weighted calculation on the wear degree data and the grinding fineness according to a weight allocation strategy to obtain the wear degree data of the grinding tool bits; according to the method, a plurality of grinding tool bits are subjected to high-fineness grinding to obtain comprehensive fineness, the grinding tool bits are subjected to fineness sequencing again, and the areas with more defects are subjected to high-fineness grinding preferentially, so that the key areas are treated more finely, the grinding efficiency reduction caused by excessive abrasion of the tool bits is avoided, and the overall quality of the surface of the casting is remarkably improved.

The invention relates to the technical field of Mini-LED chip detection, and discloses a Mini-LED repair method and device based on automatic optical detection, and the method comprises the steps: carrying out the Gaussian filtering of an optical chip image, obtaining a filtered chip image, training a neural network based on an original chip image set, obtaining a defect region detection model, and carrying out the detection of a defect region. Detecting the filter chip image according to the defect area detection model to obtain a defect image area group, if the defect image area group is not a null set, marking a to-be-repaired product as a repairable product, carrying out local contrast enhancement on the filter chip image to obtain a target chip image, carrying out single chip identification on the target chip image, and if the defect image area group is not a null set, marking the to-be-repaired product as a repairable product; and obtaining a single chip area set, identifying a to-be-maintained chip area group according to the single chip area set, and generating a repair opinion report based on the to-be-maintained chip area group. According to the invention, the precision of Mini-LED product defect detection can be improved, and the manpower consumption of Mini-LED products can be reduced.

The invention discloses a pipeline defect identification method, and relates to the technical field of pipeline defect identification, and the method comprises the following steps: obtaining a second judgment region based on a pipeline binary image; acquiring a boundary contour based on the second judgment region; acquiring a reference coordinate point based on the boundary contour; acquiring a first real-time reference value and a second real-time reference value based on the reference coordinate point and the boundary contour; acquiring a first reference threshold value and a second reference threshold value based on the images of the second number of pipeline defects; based on the first real-time reference value, the second real-time reference value, the first reference threshold value and the second reference threshold value, judging whether a defect area exists or not; the method is used for solving the problem that in an existing pipeline defect recognition technology, the defect recognition accuracy is reduced due to the fact that the repaired image of the pipeline is highly similar to the image of the pipeline defect.

The invention discloses a defect detection method, device and equipment based on a multi-field light source and a binocular camera and a storage medium, and relates to the technical field of image recognition, and the defect detection method based on the multi-field light source and the binocular camera comprises the following steps: collecting initial image data of multiple groups of binocular cameras for the same defect area under light sources of a bright field, a dark field and a side field, three-dimensional images under three light sources are generated through stereo correction and matching, and then defect detection is carried out to obtain a bright field defect detection result, a dark field defect detection result and a side field defect detection result; and when at least two of the detection results are inconsistent, fusing the three three-dimensional images, and performing defect detection on the fused image to obtain a target defect detection result. Through the closed-loop process of multi-light three-dimensional parallelism and conflict fusion re-detection, the detection accuracy and the recognition rate of the surface defects of the plates and the roller products are improved.