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

Autor:	Yang, Haoyu, Li, Zongyi, Sastry, Kumara, Mukhopadhyay, Saumyadip, Anandkumar, Anima, Khailany, Brucek, Singh, Vivek, Ren, Haoxing
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence J.6 B.7.2
Druh dokumentu:	Working Paper
Popis:	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. Comment: 9 pages, 10 figures, in preparation for journal submission
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.04056 Zobrazit plný text záznamu View this record from Arxiv