Label-free neural networks-based inverse lithography technology
Jingtao Chen, Yuanyuan Zhao, Yang Zhang, Jianxin Zhu, Xuan‐Ming Duan
Abstract
Neural network-based inverse lithography technology (NNILT) has been used to improve the computational efficiency of large-scale mask optimization for advanced photolithography. NNILT is now mostly based on labels, and its performance is affected by the quality of labels. It is difficult for NNILT to achieve high performance and extrapolation ability for mask optimization without using labels. Here, we propose a label-free NNILT (LF-NNILT), which is implemented completely without labels and greatly improves the printability of the target layouts and the manufacturability of the synthesized masks compared to the traditional ILT. More importantly, the optimization speed of LF-NNILT is two orders of magnitude faster than the traditional ILT. Furthermore, LF-NNILT is simpler to implement and can achieve better solvers to support the development of advanced lithography.