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Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training

Yang, Haoyu and Li, Zongyi and Sastry, Kumara and Mukhopadhyay, Saumyadip and Anandkumar, Anima and Khailany, Brucek and Singh, Vivek and Ren, Haoxing (2022) Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220714-224555708

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Abstract

Machine learning techniques have been extensively studied for mask optimization problems, aiming at better mask printability, shorter turnaround time, better mask manufacturability, and so on. However, most of these researches are focusing on the initial solution generation of small design regions. To further realize the potential of machine learning techniques on mask optimization tasks, we present a Convolutional Fourier Neural Operator (CFNO) that can efficiently learn layout tile dependencies and hence promise stitch-less large-scale mask optimization with the limited intervention of legacy tools. We discover the possibility of litho-guided self-training (LGST) through a trained machine learning model when solving non-convex optimization problems, which allows iterative model and dataset update and brings significant model performance improvement. Experimental results show that, for the first time, our machine learning-based framework outperforms state-of-the-art academic numerical mask optimizers with an order of magnitude speedup.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.48550/arXiv.2207.04056arXivd
ORCID:
AuthorORCID
Li, Zongyi0000-0003-2081-9665
Anandkumar, Anima0000-0002-6974-6797
Record Number:CaltechAUTHORS:20220714-224555708
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220714-224555708
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115591
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:15 Jul 2022 23:29
Last Modified:15 Jul 2022 23:29

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