AdaBalGAN: An Improved Generative Adversarial Network With Imbalanced Learning for Wafer Defective Pattern Recognition

Wang, Junliang; Yang, Zhengliang; Zhang, Jie; Zhang, Qihua; Chien, Wei-Ting Kary

doi:10.1109/tsm.2019.2925361

Cited by 114 publications

(32 citation statements)

References 39 publications

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“…However, the training of the two networks can be quite unstable and may suffer mode collapse problem [391], [392]. Wang et al in [94] proposed an improved GAN model integrated with CNN and other deep learning models called adaptive balancing generative adversarial network (AdaBal-GAN) for identification of defective patterns in WMs. This algorithm was specially proposed to enhance the classification results against imbalanced datasets.…”

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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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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“…GANs have predominantly been used in computer vision, including but not limited to image generation, face synthesis [8], image translation [9,10,11], texture synthesis [12,13], medical imaging, [14] and super-resolution [15]. Moreover, GANs can be applied in many other fields including but not limited to voice and speech signals [16,17,18], anomaly detection [19], power systems and smart grids [20,21,22], electronics [23,24], and fault diagnosis [25,26,27,28].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

et al. 2021

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“…Sejune Cheon given an automatic defect classification method based on deep learning that automatically classifies various types of wafer damage by adopting a single CNN model to extract features without additional feature extraction algorithms [26]. Unsupervised learning networks such as encoder-decoder neural network [27], and generative adversarial network [28] are used for wafer defect feature segmentation recently.…”

Section: Introductionmentioning

confidence: 99%

A Light-Weighted CNN Model for Wafer Structural Defect Detection

Chen

Han

et al. 2020

IEEE Access

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Silicon wafer is the raw material of semiconductor chip. It is important and challenging to research a fast and accurate method of identifying and classifying wafer structural defects. To this end, we present a novel detection method in terms of the convolution neural networks (CNN), which achieve more than 99% detection accuracy. Due to the wafer images are not available by open datasets, a set of imaging acquisition system is designed to capture wafer images. Digital image preprocessing technology is utilized to split a wafer image into thousands of silicon grain images. The proposed model, called WDD-Net, uses depthwise separable convolutions and global average pooling to reduce parameters and calculations, adopts multiple 1 * 1 standard convolutions to increase the network depth. Specifically, two types of CNN models, VGG-16 and MobileNet-v2, are adopted for comparative analysis. Using the aforementioned three models, the comparative experiments are implemented on data sets that consisting of more than ten thousand grain images. The experimental results show that compared with VGG-16 and MobileNet-v2, the detection speed of the WDD-Net is 105.6FPS, which is 5 times faster. The model size of the WDD-Net is 307KB, which is much smaller than the other two. Furthermore, the WDD-Net directly completes the data collection and defect detection process through the local computing equipment, which is suitable for edge computing. INDEX TERMS Image classification, neural networks, semiconductor manufacturing, machine learning.

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AdaBalGAN: An Improved Generative Adversarial Network With Imbalanced Learning for Wafer Defective Pattern Recognition

Cited by 114 publications

References 39 publications

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

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

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

A Light-Weighted CNN Model for Wafer Structural Defect Detection

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