AI-powered deconvolution-based super-resolution imaging for semiconductor OCD metrology and precise stage positioning

Yu-Ting Cheng; Wei-Yun Lee; Ming-Jie Liu; Wei-Hsin Chein; Liang-Chia Chen

doi:10.1117/12.3010986

9 April 2024 AI-powered deconvolution-based super-resolution imaging for semiconductor OCD metrology and precise stage positioning

Yu-Ting Cheng, Wei-Yun Lee, Ming-Jie Liu, Wei-Hsin Chein, Liang-Chia Chen

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129553D (2024) https://doi.org/10.1117/12.3010986
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

This study develops a parametric system transfer function (STF) model using scalar diffraction theory and Fourier optics to address the loss of precision in image-based positioning caused by the diffraction limit on marker scale. By fitting the model to observed STFs and employing deconvolution and a deep convolutional neural network, the method enhances image quality, overcoming traditional deconvolution limitations. Applied to critical dimension measurements, it improved radius accuracy for vias and pillars by 54.8% and reduced displacement measurement bias by 36.4%. The development particularly benefits automatic optical inspection (AOI) for quality control in semiconductor manufacturing.

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

Citation Download Citation

Yu-Ting Cheng, Wei-Yun Lee, Ming-Jie Liu, Wei-Hsin Chein, and Liang-Chia Chen "AI-powered deconvolution-based super-resolution imaging for semiconductor OCD metrology and precise stage positioning", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129553D (9 April 2024); https://doi.org/10.1117/12.3010986

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