Decentralized data, centralized insights: a federated machine learning framework for SEM based defect classification and detection in semiconductor manufacturing

Bappaditya Dey; Jacob Deforce; Victor Blanco; Sandip Halder; Philippe Leray

doi:10.1117/12.3023126

10 April 2024 Decentralized data, centralized insights: a federated machine learning framework for SEM based defect classification and detection in semiconductor manufacturing

Bappaditya Dey, Jacob Deforce, Victor Blanco, Sandip Halder, Philippe Leray

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129553V (2024) https://doi.org/10.1117/12.3023126
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

In the face of the escalating challenges posed by Moore's Law, semiconductor manufacturers find themselves compelled to explore innovative techniques aimed at enhancing the density of chip components. The reduction in pitches to sizes below 32nm and the adoption of advanced semiconductor packaging technologies have become common strategies. However, this evolution in chip architecture has led to an increase in nano-scale defects, necessitating the development of an ADCD framework. Yet, the scarcity of comprehensive data for training inspection models impedes progress. SEM imaging and manual defect labelling are resource-intensive, and the sensitivity of wafer images precludes data sharing among different foundries/users/manufacturers. Moreover, the variability in defect classes and occurrences across different clients further complicates the development of a universally applicable model. To address these challenges, we propose a decentralized Federated Learning framework utilizing the YOLOv8 object detection model. By securely leveraging the diverse datasets among participants without exchanging sensitive information, our approach aims to create a more generalizable model.

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

Citation Download Citation

Bappaditya Dey, Jacob Deforce, Victor Blanco, Sandip Halder, and Philippe Leray "Decentralized data, centralized insights: a federated machine learning framework for SEM based defect classification and detection in semiconductor manufacturing", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129553V (10 April 2024); https://doi.org/10.1117/12.3023126

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