Adversarial Defect Detection in Semiconductor Manufacturing Process

Detecting defects in the inspection stage of semiconductor manufacturing process is a crucial task to improve yield and productivity as well as wafer quality. Recent Advances in semiconductor process technology have greatly increased the transistor density. As a result, an increasingly high number o...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing 2021-08, Vol.34 (3), p.365-371
Main Authors: Kim, Jaehoon, Nam, Yunhyoung, Kang, Min-Cheol, Kim, Kihyun, Hong, Jisuk, Lee, Sooryong, Kim, Do-Nyun
Format: Article
Language:English
Subjects:
adversarial network
Computer architecture
deep learning
defect detection
Defects
Heating systems
Inspection
Layout
Lithography pattern
Model testing
Object detection
Scanning electron microscopy
Semiconductor device modeling
semiconductor manufacturing
Semiconductor process modeling
Transistors
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Summary:Detecting defects in the inspection stage of semiconductor manufacturing process is a crucial task to improve yield and productivity as well as wafer quality. Recent Advances in semiconductor process technology have greatly increased the transistor density. As a result, an increasingly high number of defects inevitably emerge and we need a more accurate and efficient detection method to manage them. In this paper, we propose a deep-learning-based defect detection model to expedite the process. It adopts an adversarial network architecture of conditional GAN. The discriminator of an adversarial network architecture helps the detection model learn to detect and classify defects accurately. The high performance is achieved by using Focal Loss, PixelGAN and multi-scale level features, which is shown to be better than the baseline model, CenterNet, when tested for a real industrial dataset.
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2021.3089869