Wafer Defect Pattern Recognition and Analysis Based on Convolutional Neural Network

Yu, Naigong; Qiao, Xu; Wang, Honglu

doi:10.1109/tsm.2019.2937793

Cited by 80 publications

(25 citation statements)

References 20 publications

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“…To address the phenomenon of image whitening and insufficient compensation caused by log-transforming images with too much luminance, exponential transformation was used in this study to deal with such images for effective compensation of the images ( Yu et al, 2019 ; Tian et al, 2020 ). The exponential transformation Equation is as follows.…”

Section: Methodsmentioning

confidence: 99%

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Wang

Zhao

et al. 2022

Front. Physiol.

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The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients’ condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.

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Section: Methodsmentioning

confidence: 99%

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Wang

Zhao

et al. 2022

Front. Physiol.

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“…During production, wafers get inspected to identify localized defects [11,12,13] and defect patterns in WDMs [9,14,15,16,17,18,19,20], which is the problem we address in our work. Wafer Defect Maps are lists containing the coordinates at which inspection machines find defects, and these correspond to huge binary images (in our case 20, 000 × 20, 000).…”

Section: Wafer Monitoringmentioning

confidence: 99%

“…Since Convolutional Neural Networks have achieved impressive results in image classification, the most recent methods [17,18,19,20] employ Deep Learning models to classify Wafer Bin Maps. In particular, [17] addresses the simplified problem of distinguishing radial map patterns from non-radial ones.…”

Section: Wafer Monitoringmentioning

confidence: 99%

Deep open-set recognition for silicon wafer production monitoring

Frittoli

Carrera²,

Rossi³

et al. 2022

Pattern Recognition

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“…However, they took much longer training time than FCN. Yu et al in [92] constructed an algorithm based on CNN to recognize and classify WM defects. The proposed method was divided into two parallel approaches: offline modelling and online processing.…”

Section: Deep Learningmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Wafer Defect Pattern Recognition and Analysis Based on Convolutional Neural Network

Cited by 80 publications

References 20 publications

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Deep open-set recognition for silicon wafer production monitoring

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

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