Tackling data inconsistency and runtime issues in inverse lithography technology (ILT) with comparative convergence study

Po-Hsun Fang; Peichen Yu

doi:10.1117/12.3007748

10 April 2024 Tackling data inconsistency and runtime issues in inverse lithography technology (ILT) with comparative convergence study

Po-Hsun Fang, Peichen Yu

Author Affiliations +

Proceedings Volume 12954, DTCO and Computational Patterning III; 129541E (2024) https://doi.org/10.1117/12.3007748
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

This study delves into the convergence challenges of Inverse Lithography Technology (ILT) in advancing Optical Proximity Correction (OPC). While ILT shows promise, it faces runtime hurdles and intricate stitching issues caused by data inconsistencies at tile boundaries, particularly in complex corrections. Here, we perform a comprehensive and comparative analysis of the run time and data consistency at tile boundaries for four gradient descent-based algorithms: Steepest Descent (SD), Momentum, Adaptive gradient, and Adaptive Moment Estimation (Adam). Our findings reveal that stitching problems arise from insufficient ambient range and convergence issues during ILT optimization. We recommend using an ambient size equal to or larger than the kernel size. Furthermore, we show that robust convergence can mitigate data inconsistency challenges, even with a limited ambient range. Notably, Adam emerges as a powerful solution, offering substantial runtime acceleration, often ten to hundreds of times faster than SD. Renowned for its prowess in optimizing complex models and GPU-accelerated parallel processing, Adam is a key strategy for expediting computational lithography in semiconductor manufacturing, paving the way for future advancements in ILT.

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

Citation Download Citation

Po-Hsun Fang and Peichen Yu "Tackling data inconsistency and runtime issues in inverse lithography technology (ILT) with comparative convergence study", Proc. SPIE 12954, DTCO and Computational Patterning III, 129541E (10 April 2024); https://doi.org/10.1117/12.3007748

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