Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields

Autor:	Chen, Guojin, Pei, Zehua, Yang, Haoyu, Ma, Yuzhe, Yu, Bei, Wong, Martin D. F.
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead. The advancement of machine learning (ML)-based lithography models alleviates the trade-offs between manufacturing process expense and capability. However, all previous methods regard the lithography system as an image-to-image black box mapping, utilizing network parameters to learn by rote mappings from massive mask-to-aerial or mask-to-resist image pairs, resulting in poor generalization capability. In this paper, we propose a new ML-based paradigm disassembling the rigorous lithographic model into non-parametric mask operations and learned optical kernels containing determinant source, pupil, and lithography information. By optimizing complex-valued neural fields to perform optical kernel regression from coordinates, our method can accurately restore lithography system using a small-scale training dataset with fewer parameters, demonstrating superior generalization capability as well. Experiments show that our framework can use 31% of parameters while achieving 69$\times$ smaller mean squared error with 1.3$\times$ higher throughput than the state-of-the-art. Comment: Accepted by DAC23
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.08435 Zobrazit plný text záznamu View this record from Arxiv