Fast mask near-field calculation using fully convolution network

Jiaxin Lin, Lisong Dong, Taian Fan, Xu Ma, Rui Chen, Yayi Wei
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引用次数: 8

Abstract

Near-field calculation for thick mask is a fundamental task in lithography simulations. This paper proposes a fully convolution network (FCN) method to improve the efficiency of three-dimensional (3D) mask near-field calculation compared to the rigorous electromagnetic field (EMF) simulation methods. Taking into account the 3D mask effects, the network is trained based on a set of mask samples and the corresponding near-field data obtained by the EMF simulator. During the testing stage, the trained FCN is used to rapidly predict the diffraction near-field of the testing mask patterns. The performance of the proposed FCN approach is evaluated by simulations based on EUV lithography.
快速掩模近场计算使用全卷积网络
厚掩模的近场计算是光刻仿真中的一项基本任务。本文提出了一种完全卷积网络(FCN)方法,与严格的电磁场(EMF)仿真方法相比,提高了三维(3D)掩模近场计算的效率。考虑到三维掩模效应,基于一组掩模样本和EMF模拟器获得的相应近场数据对网络进行训练。在测试阶段,利用训练好的FCN快速预测测试掩模图案的衍射近场。通过基于EUV光刻技术的仿真,对该方法的性能进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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