Advancing semiconductor defect detection with integrated deep learning and color scale preprocessing

Zheng Lan; Wei Zhao; Xiupeng Shi

doi:10.1117/12.3052800

10 December 2024 Advancing semiconductor defect detection with integrated deep learning and color scale preprocessing

Zheng Lan, Wei Zhao, Xiupeng Shi

Author Affiliations +

Proceedings Volume 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024); 134230I (2024) https://doi.org/10.1117/12.3052800
Event: 8th International Workshop on Advanced Patterning Solutions (IWAPS 2024), 2024, Jiaxing, Zhejiang, China

Abstract

The increasing complexity and miniaturization of integrated circuits (ICs) have amplified the impact of minor manufacturing defects, which can significantly degrade chip performance and system reliability. Accurate classification of these subtle defects is crucial but challenging due to the intricate features in chip imagery. In this paper, we introduce VSRNet, an enhanced network architecture that leverages the strengths of VGGNet, integrated with Squeeze-and-Excitation (SE) attention mechanisms and residual connections (Res). To further boost classification accuracy, we develop a novel image preprocessing technique that amplifies subtle chromatic aberration, making minor defects more detectable. Experimental evaluations demonstrate that VSRNet consistently outperforms traditional approaches across multiple metrics, with notable improvements observed when incorporating the proposed preprocessing method. This work offers a robust solution for fine-grained defect classification in complex semiconductor manufacturing environments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zheng Lan, Wei Zhao, and Xiupeng Shi "Advancing semiconductor defect detection with integrated deep learning and color scale preprocessing", Proc. SPIE 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024), 134230I (10 December 2024); https://doi.org/10.1117/12.3052800

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KEYWORDS

Chromatic aberrations

Defect detection

Image classification

Deep learning

Semiconductors

Feature extraction

Image enhancement

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