Anomaly Detection and Segmentation for Wafer Defect Patterns Using Deep Convolutional Encoder–Decoder Neural Network Architectures in Semiconductor Manufacturing

Nakazawa, Takeshi; Kulkarni, Deepak V.

doi:10.1109/tsm.2019.2897690

Cited by 120 publications

(29 citation statements)

References 13 publications

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“…ey used the proportional coefficient of soft shrinkage threshold to optimize DCNN, which was found to be applicable to noise removal in the image [12]. Nakazawa and Kulkarni proposed a method to detect and segment abnormal defect patterns using the NN architecture of deep convolutional encoder-decoder and finally found that these models could detect invisible defect patterns from real images [13].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of Feature Extraction and Anti‐Interference of Face Image under Deep Reconstruction Network Algorithm

et al. 2021

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In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network (CNN) reconstructed by deep learning (DL) framework in face image feature extraction (FE) and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent (SGD) and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild (LFW) face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1 (accuracy: 99.03%, loss: 0.0047, training time: 352 s, and overfitting rate: 1.006), and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time (206.44 s) and a higher correct matching rate (88.75%) than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of Feature Extraction and Anti‐Interference of Face Image under Deep Reconstruction Network Algorithm

et al. 2021

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“…Furthermore, this study does not provide any information about the defect cluster, size and its location. In a later study conducted by the same authors in [107], Nakazawa and Kulkarni overcame the lack of information problem using different type of CNN models such as Fully Convolutional Network (FCN) [402], SegNet [403] and U-Net [404]. These models were compared to each other with regards to their classification performance and training time.…”

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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“…Nakazawa and Kulkarni [29] presented SegNet, U-Net, and FCN based autoencoders for detection and segmentation of abnormal wafer map defect patterns. SegNet and U-net based networks showed similar training accuracy, while FCN based architecture performed lower than them.…”

Section: ) Auto_encoder (Ae)mentioning

confidence: 99%

A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition

et al. 2021

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Anomaly Detection and Segmentation for Wafer Defect Patterns Using Deep Convolutional Encoder–Decoder Neural Network Architectures in Semiconductor Manufacturing

Cited by 120 publications

References 13 publications

[Retracted] Analysis of Feature Extraction and Anti‐Interference of Face Image under Deep Reconstruction Network Algorithm

[Retracted] Analysis of Feature Extraction and Anti‐Interference of Face Image under Deep Reconstruction Network Algorithm

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

A Systematic Review of Deep Learning for Silicon Wafer Defect Recognition

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