Application of the Attention-Guided Neural Network for Defect Detection

Abstract The Attention-Guided Neural Network is designed to analyze periodic SEM images of SRAM. Autoencoder latent features layer is used to reconstruct the crops of the original image. By thresholding the ability of the autoencoder to reconstruct the original image, the defects and artifacts of the original image are automatically located. This approach could be used to detect visual anomalies.

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Publication Details

Journal
Proceedings - International Symposium for Testing and Failure Analysis
Published
2024-10-23
DOI
https://doi.org/10.31399/asm.cp.istfa2024p0009
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
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article

Application of the Attention-Guided Neural Network for Defect Detection

Alexey Solovey, Jane Y. Li, Chuan Zhang
Proceedings - International Symposium for Testing and Failure Analysis
Industrial Vision Systems and Defect Detection
article

Application of the Attention-Guided Neural Network for Defect Detection

Alexey Solovey, Jane Y. Li, Chuan Zhang
article en

Abstract

Abstract The Attention-Guided Neural Network is designed to analyze periodic SEM images of SRAM. Autoencoder latent features layer is used to reconstruct the crops of the original image. By thresholding the ability of the autoencoder to reconstruct the original image, the defects and artifacts of the original image are automatically located. This approach could be used to detect visual anomalies.

Proceedings - International Symposium for Testing and Failure AnalysisVol. 84918
Nvidia (United States) (US)
Openalex Percentile: Top 73%
Industrial Vision Systems and Defect Detection
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Application of the Attention-Guided Neural Network for Defect Detection — Alexey Solovey, Jane Y. Li, et al. · Proceedings - International Symposium for Testing and Failure Analysis (2024) | TGRS Research Map | TGRS