Automatic wafer defect detection and accurate classification using machine learning based analysis of SEM images

Sanghyun Choi; Nathan Greeneltch; Mohan Govindaraj; Srividya Jayaram; Mark Pereira; Sayani Biswas; Samir Bhamidipati; Ilhami Torunoglu

doi:10.1117/12.3031212

18 September 2024 Automatic wafer defect detection and accurate classification using machine learning based analysis of SEM images

Sanghyun Choi, Nathan Greeneltch, Mohan Govindaraj, Srividya Jayaram, Mark Pereira, Sayani Biswas, Samir Bhamidipati, Ilhami Torunoglu

Author Affiliations +

Proceedings Volume 13273, 39th European Mask and Lithography Conference (EMLC 2024); 132731L (2024) https://doi.org/10.1117/12.3031212
Event: 39th European Mask and Lithography Conference (EMLC 2024), 2024, Grenoble, France

Abstract

As advanced semiconductor technologies continuing to evolve, defect detection and classification have experienced increased challenges because the complex process involved with greatly increased transistor density resulting in high defect rates. This makes it more challenging than before for traditional analysis done by human experts prone to error due to long hours of focus required. Software with rules may be used to overcome this, but still faces the issue to construct them, since image quality varies from process to process and layer to layer. In this paper we propose using a machine learning (ML) model, YOLO (You Only Look Once) v8, that will learn complex rules that can generalize to various image qualities and accurately detect defects without human intervention.

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

Citation Download Citation

Sanghyun Choi, Nathan Greeneltch, Mohan Govindaraj, Srividya Jayaram, Mark Pereira, Sayani Biswas, Samir Bhamidipati, and Ilhami Torunoglu "Automatic wafer defect detection and accurate classification using machine learning based analysis of SEM images", Proc. SPIE 13273, 39th European Mask and Lithography Conference (EMLC 2024), 132731L (18 September 2024); https://doi.org/10.1117/12.3031212

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