393 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 belongs to the technical field of image data processing, and particularly relates to a quality detection method and system for metal pipe fitting forming, and the method comprises the steps: obtaining a plurality of groups of matched gap regions of a port edge image according to the gray scale difference of the neighborhood of each gap of the port edge image in a corresponding metal pipe fitting image; obtaining a suspected defect area of the port edge image according to curvature change of edge pixel points of the port edge image; acquiring similar arc segments of each suspected defect area according to the change of the distance from the arc segment on the ellipse of the metal pipe edge image to the ellipse center; obtaining the abnormal degree of each suspected defect region according to the deformation difference between the suspected defect region and the similar arc section and the gray difference between the suspected defect region and the corresponding suspected defect region of the same group of matched gap regions; and screening to obtain a final defect area. According to the invention, the accuracy of quality forming detection of the metal pipe fitting is improved.

The invention discloses a fabric defect detection method based on image reconstruction, which is characterized in that a U-shaped self-coding model with a space self-attention module, a channel self-attention module and jump connection is used, and only a defect-free normal image is used during model training; according to the method, a diffusion process of gradually adding noise and an anti-diffusion process of gradually removing noise are carried out, so that the model learns to gradually remove mixed noise in an image, a defect image with noise is reconstructed into a noiseless and defect-free image by the model in a test stage, and the steps of acceleration, visualization of a defect area and the like are realized by using a step length resetting method. Compared with the prior art, the method provided by the invention has the advantages that model training can be carried out only by using a normal image, the detection effect can be improved, a defect area with pixel-level fineness can be output, meanwhile, the denoising frequency during detection is reduced, the real-time performance of defect detection is improved, the problem of too long reconstruction time caused by repeated denoising of the model is relieved, and the reconstruction efficiency is improved. Good application prospects are realized.

A method of repairing a defect on a surface is presented that includes imaging the surface to locate the defect with a first imaging system. The method also includes conducting a repair operation by contacting the surface with an abrasive article. The abrasive article is pressed into contact with the surface in an area of the defect by a robotic repair system. The method also includes imaging the abraded surface, with a second imaging system. Imaging includes scanning the surface in the defect area to obtain a topography of the defect area, passing the second imaging system over the defect area such that a distance between the second imaging system and the surface is maintained. Imaging also includes generating an image of the defect area, wherein the image is a near dark field image or a dark field image and generating an evaluating regarding the repair operation based on the generated image.

The invention relates to the technical field of defect detection, in particular to a defect detection method, system and equipment based on improved YOLOv5 and a medium. Firstly, image data of industrial parts in the production process are collected through a high-definition camera, image preprocessing is carried out according to part surface defect characteristics, key frames are extracted, defect areas are marked, and a defect detection data set is constructed; secondly, configuring a training environment; constructing a CED-YOLO network model by taking the YOLOv5 network as a basic network, replacing a C3 module in a backbone network of the YOLOv5 network with a CEMS module as a feature extraction module, replacing a C3 module of a neck network connection detection layer part with differential attention DA, and enhancing extracted features; and finally, the CED-YOLO network model is trained and verified by using the data set after data enhancement, so that the calculation amount is effectively reduced, the detection efficiency is improved, and the detection performance of the model is greatly 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 provides an intelligent visual inspection method and system for hardware stamping die production, and relates to the field of hardware stamping die production, and the method comprises the steps: obtaining a die current state image of a to-be-detected hardware stamping die and a defect-free die state reference image, and carrying out the geometric alignment; and then calculating a difference graph to obtain significance distribution of a potential defect area, carrying out multi-scale difference feature hierarchical coding by using a pyramid network, and separating normal wear artifacts from real defects. Furthermore, a feature receptive field anchoring mechanism is introduced, and the attention weight is dynamically adjusted according to the geometric topology of the mold. And finally, a lightweight classifier is used to make a decision on the enhanced difference features, and sub-millimeter-level anomaly recognition is realized. In this way, the sensitivity to tiny changes of the hardware stamping die can be effectively improved, and defects can be found in the early stage.

The invention relates to the technical field of defect detection, in particular to a chip defect detection method and system based on a Gaussian attention mechanism, and the method comprises the steps: collecting a chip image, carrying out the preprocessing of the collected chip image, marking a defect region in the chip image, and generating a binary mask image reflecting the defect distribution; based on the binary mask image, combining distance transformation and local color change analysis to generate a Gaussian attention channel; connecting the Gaussian attention channel with the chip image in the channel dimension to generate an enhanced image; and executing a defect detection algorithm based on the enhanced image, and identifying a defect region in the chip image. According to the method, the sensitivity of the detection system to tiny and low-contrast defects can be improved, so that the false alarm rate is reduced, and meanwhile, the defect detection precision is effectively improved on the premise that the calculation complexity is not greatly increased.

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 discloses a defect detection method and system for an electronic display panel, and is applied to the technical field of computer vision, and the method comprises the steps: obtaining image data of a to-be-detected display panel; inputting the image data into a pre-trained neural network model to obtain the position and the type of the defect area of the display panel; wherein the neural network model comprises a backbone network, a neck network and a detection head, and the backbone network is used for performing feature extraction on the image data to obtain a plurality of initial feature maps; the neck network is used for performing feature interaction on the plurality of initial feature maps to obtain target feature maps respectively corresponding to the plurality of initial feature maps; and the detection head is used for performing target detection based on the target feature maps corresponding to the plurality of initial feature maps to obtain the position and the type of the defect area of the display panel. The defect detection precision of the display panel can be improved.

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 relates to the technical field of electric signal processing, in particular to an electric connector service life prediction method and system. The method comprises the following steps: continuously monitoring the electrical properties of the contact element of the electric connector, and recording the change of a contact resistance value as an abnormal initial site when the change of the contact resistance value is detected; acquiring a surface image of the electric connector according to the abnormal initial site; contact surface abrasion and scratches of the surface image of the electric connector are detected, and connector defects of the contact surface are recognized and marked as a connector structure defect area; carrying out area overall physical hardness detection on the defect area of the connector structure to obtain the overall hardness amount of the connector area; and carrying out hardness sudden change part identification on the overall hardness amount of the connector region, and recording as a connector hardness abnormal region. According to the invention, through an electric signal processing technology and an image identification technology, comprehensive evaluation of the performance of the electric connector is realized, so that the accuracy of predicting the service life of the electric connector is improved.

The invention discloses an intelligent sorting method for identifying semiconductor crystal growth defects, relates to the technical field of visual identification, and solves the problem of inaccurate identification of defect types in current wafer detection.The method comprises the steps that a defect image of a wafer is standardized according to historical defect data, and a standard defect image of the wafer is obtained through standardization; detecting a defect area of the wafer based on the standard defect image; detecting the binary image of the wafer, and judging the defect type of the wafer according to a detection result; constructing a defect type of the wafer and a corresponding defect judgment rule based on the defect data; the visual model is trained by using the defect type of the wafer and the corresponding defect judgment rule, the corresponding visual model is selected as the preferred visual model according to the training result of the visual model and the defect type of the wafer, and accurate detection and accurate identification of the defect type of the wafer are realized.

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 discloses a component defect detection method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a three-dimensional model of a component; collecting laser ultrasonic data of the three-dimensional model, and collecting a thermal imaging image and a polarized light image of the assembly; based on the laser ultrasonic data, the thermal imaging image and the polarized light image, predicting to obtain a first defect category and a first defect position corresponding to a defect area of the assembly; based on the defect category and the defect position of the defect area, reconstructing three-dimensional defect data of the component; and on the basis of the three-dimensional defect data, predicting to obtain an expansion direction and an expansion range of the defect area.

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 embodiments of the present application provide an image detection method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring an image to be detected; using a preset defect detection algorithm, performing defect detection on the image, so as to determine a first defect detection result, the first defect detection result comprising position information of a defect area in the image to be detected; based on the first defect detection result, acquiring a defect-free image from the image to be detected; using the defect-free image to train a defect detection model, so as to acquire a trained defect detection model; at least using the trained defect detection model, performing defect detection on a target image, so as to determine a second defect detection result, the second defect detection result comprising position information of a defect area in the target image, and the target image being an image to be detected, or a defective image obtained from the image to be detected based on the first defect detection result. The method helps to improve the accuracy and robustness of image detection.

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 provides a defect detection method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: providing a wafer; a sampling image containing the wafer is obtained, the sampling image is obtained when the wafer is irradiated by light, the sampling image comprises a color area and a gray area, the color area is an area where defects are located, the defects of different types have different colors, and the gray area is a non-defect area; according to the color area, the sampling image is extracted, a plurality of sub-images are obtained, and one sub-image comprises the color area of one defect type; inputting the plurality of sub-images into a defect classification model, and determining a defect type corresponding to each sub-image; wherein an input layer of the defect classification model comprises a plurality of input channels, and each input channel is in one-to-one correspondence with the color of one defect type. By adopting the technical scheme, the detection precision of defect types can be improved.

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 relates to the technical field of photovoltaic chip packaging, in particular to a detection system for packaging a high-power photovoltaic TMBS chip, which adopts a long-short-term memory network to analyze the duration, power change and heat dissipation efficiency in a welding spot group, combines parameters with the correlation degree of defect occurrence, effectively predicts the occurrence of defects and identifies potential high-incidence paths, and improves the detection accuracy of the high-power photovoltaic TMBS chip packaging. The method has advantages in time sequence data processing, processes complex nonlinear relations, ensures accurate identification of defect trends, analyzes contour and deformation of welding spots and pixel features of contact areas through an image identification technology, calculates deformation deviation, contour closure and pixel loss rate, screens out defect areas, improves defect screening accuracy, and improves detection accuracy. According to the method, defects of different types are effectively distinguished, a generative adversarial network is used for tracking a crack point area, the offset and diffusion trend of a crack point path are calculated, occurrence and propagation of crack points are effectively positioned, and the packaging quality and long-term stability of a photovoltaic chip are improved.

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.