A Light-Weighted CNN Model for Wafer Structural Defect Detection

Chen, Xiaoyan; Chen, Jianyong; Han, Xiaoguang; Zhao, Chundong; Zhang, Dongyang; Zhu, Kuifeng; Su, Yanjie

doi:10.1109/access.2020.2970461

Cited by 58 publications

(24 citation statements)

References 26 publications

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“…In another study, Xiaoyan et al [ 20 ] developed a lightweight CNN model dubbed ‘WDD-Net’ for silicon wafer structural defect detection with a very high detection accuracy (99%). The research evaluated the WDD-Net model against two other established CNN models, one based on VGG-16 and the other based on MobileNet-v2, in which the experimental results showed that WDD-Net was five times faster than the 307 KB models, hence the term ‘lightweight’.…”

Section: Introductionmentioning

confidence: 99%

An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS

Amini

Kanfoud

Gan

2021

Sensors

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With the advancement of miniaturization in electronics and the ubiquity of micro-electro-mechanical systems (MEMS) in different applications including computing, sensing and medical apparatus, the importance of increasing production yields and ensuring the quality standard of products has become an important focus in manufacturing. Hence, the need for high-accuracy and automatic defect detection in the early phases of MEMS production has been recognized. This not only eliminates human interaction in the defect detection process, but also saves raw material and labor required. This research developed an automated defects recognition (ADR) system using a unique plenoptic camera capable of detecting surface defects of MEMS wafers using a machine-learning approach. The developed algorithm could be applied at any stage of the production process detecting defects at both entire MEMS wafer and single component scale. The developed system showed an F1 score of 0.81 U on average for true positive defect detection, with a processing time of 18 s for each image based on 6 validation sample images including 371 labels.

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Section: Introductionmentioning

confidence: 99%

An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS

Amini

Kanfoud

Gan

2021

Sensors

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“…In several works, the defect classification from wafer and mask data with deep learning methods has been demonstrated. [7][8][9][10][11][12][13] Such methods also have been used to perform pattern matching, contour extraction, and 3D profile reconstruction from SEM images. [14][15][16] A general property of SEM images, the noise, has been addressed in further works, with goal of reducing the noise to obtain higher accuracy in the image analysis.…”

Section: Introductionmentioning

confidence: 99%

Mask defect detection with hybrid deep learning network

Evanschitzky

Auth

Heil

et al. 2021

J. Micro/Nanopattern. Mats. Metro.

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Background: Deep learning is a very fast-growing field in the area of artificial intelligence with remarkable results in recent years. Many works in the lithography and photomask field have shown progress of technology in this application area and the potential to improve lithography by the automation of processes. Despite this progress, the use of machine learning techniques in the field of mask repair still seems to be at the beginning.Aim: We show that deep learning-based methods can successfully be applied to mask repair applications.Approach: The presented system is a hybrid and modular approach based on a combination of several deep learning networks and analytical methods, enabling the detection of mask pattern defects and the determination of the exact defect shapes from SEM images. In the current version, the system is trained for line/space patterns and contact patterns with typical defect types. The modularity allows for the extensibility to new use cases. The issue of an insufficient amount of training data is addressed using purely computer-generated simplified SEM data in combination with a specific network architecture. Results:The very good functionality and defect detection accuracy of the system are demonstrated with a set of real SEM images with line/space patterns and contact patterns with numerous defects. In particular, a 100% true defect detection rate could be obtained. Conclusions:The presented machine learning approach demonstrates the successful defect identification, location, and shape determination from real mask SEM images.

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“…Since the collection of defect images is time-consuming, recent research on generating pseudo defective images with GAN has attracted attention. Chen et al [12] uses affine transformation and naïve generative adversarial networks (GAN) to tackle the problem of having unbalanced quantities of defect-free and defective images. They expanded the number of defective images that enhanced the classifier's generalization ability.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Measuring Defects in Wafer Die Using GAN and YOLOv3

2020

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This research used deep learning methods to develop a set of algorithms to detect die particle defects. Generative adversarial network (GAN) generated natural and realistic images, which improved the ability of you only look once version 3 (YOLOv3) to detect die defects. Then defects were measured based on the bounding boxes predicted by YOLOv3, which potentially provided the criteria for die quality sorting. The pseudo defective images generated by GAN from the real defective images were used as the training image set. The results obtained after training with the combination of the real and pseudo defective images were 7.33% higher in testing average precision (AP) and more accurate by one decimal place in testing coordinate error than after training with the real images alone. The GAN can enhance the diversity of defects, which improves the versatility of YOLOv3 somewhat. In summary, the method of combining GAN and YOLOv3 employed in this study creates a feature-free algorithm that does not require a massive collection of defective samples and does not require additional annotation of pseudo defects. The proposed method is feasible and advantageous for cases that deal with various kinds of die patterns.

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A Light-Weighted CNN Model for Wafer Structural Defect Detection

Cited by 58 publications

References 26 publications

An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS

An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS

Mask defect detection with hybrid deep learning network

Detecting and Measuring Defects in Wafer Die Using GAN and YOLOv3

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