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Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks

Yang, Haoyu and Li, Zongyi and Sastry, Kumara and Mukhopadhyay, Saumyadip and Kilgard, Mark and Anandkumar, Anima and Khailany, Brucek and Singh, Vivek and Ren, Haoxing (2022) Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220714-224607394

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Abstract

Lithography simulation is a critical step in VLSI design and optimization for manufacturability. Existing solutions for highly accurate lithography simulation with rigorous models are computationally expensive and slow, even when equipped with various approximation techniques. Recently, machine learning has provided alternative solutions for lithography simulation tasks such as coarse-grained edge placement error regression and complete contour prediction. However, the impact of these learning-based methods has been limited due to restrictive usage scenarios or low simulation accuracy. To tackle these concerns, we introduce an dual-band optics-inspired neural network design that considers the optical physics underlying lithography. To the best of our knowledge, our approach yields the first published via/metal layer contour simulation at 1nm^2/pixel resolution with any tile size. Compared to previous machine learning based solutions, we demonstrate that our framework can be trained much faster and offers a significant improvement on efficiency and image quality with 20X smaller model size. We also achieve 85X simulation speedup over traditional lithography simulator with 1% accuracy loss.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.48550/arXiv.2203.08616arXivDiscussion Paper
ORCID:
AuthorORCID
Li, Zongyi0000-0003-2081-9665
Anandkumar, Anima0000-0002-6974-6797
Additional Information:accepted at 59th Design Automation Conference.
Record Number:CaltechAUTHORS:20220714-224607394
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220714-224607394
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115594
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:15 Jul 2022 23:28
Last Modified:15 Jul 2022 23:28

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