Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis

Sanghyun Choi; Qian Xie; Nathan Greeneltch; Hyung Joo Lee; Mohan Govindaraj; Srividya Jayaram; Mark Pereira; Sayani Biswas; Samir Bhamidipati; Ilhami Torunoglu

doi:10.1117/12.3012184

9 April 2024 Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis

Sanghyun Choi, Qian Xie, Nathan Greeneltch, Hyung Joo Lee, Mohan Govindaraj, Srividya Jayaram, Mark Pereira, Sayani Biswas, Samir Bhamidipati, Ilhami Torunoglu

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129553M (2024) https://doi.org/10.1117/12.3012184
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

Performing accurate and timely Scanning Electron Microscope (SEM) image analysis to identify wafer defects is crucial as it directly impacts manufacturing yield. In this paper, a machine learning (ML) based approach for analyzing SEM images (from wafer inspection machines) to locate and classify wafer defects is proposed. A state-of-the-art one-stage objection detection model called YOLOv8 (You Only Look Once version 8) is used as it offers a good balance between accuracy and inference speed. Experimental results confirm that an ensemble model composed of multiple YOLOv8 models can predict 6 types of defects with a mean Average Precision (mAP) of 0.789 (at IoU=0.5) for unseen test data consisting of real-world SEM images from 5 wafer fabs that have varying image qualities.

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

Citation Download Citation

Sanghyun Choi, Qian Xie, Nathan Greeneltch, Hyung Joo Lee, Mohan Govindaraj, Srividya Jayaram, Mark Pereira, Sayani Biswas, Samir Bhamidipati, and Ilhami Torunoglu "Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129553M (9 April 2024); https://doi.org/10.1117/12.3012184

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