Model-informed deep learning for computational lithography with partially coherent illumination
Xianqiang Zheng, Xu Ma, Qile Zhao, Yihua Pan, Gonzalo R. Arce
Abstract
Computational lithography is a key technique to optimize the imaging performance of optical lithography systems. However, the large amount of calculation involved in computational lithography significantly increases the computational complexity. This paper proposes a model-informed deep learning (MIDL) approach to improve its computational efficiency and to enhance the image fidelity of lithography system with partially coherent illumination (PCI). Different from conventional deep learning approaches, the network structure of MIDL is derived from an approximate compact imaging model of PCI lithography system. MIDL has a dual-channel structure, which overcomes the vanishing gradient problem and improves its prediction capacity. In addition, an unsupervised training method is developed based on an accurate lithography imaging model to avoid the computational cost of labelling process. It is shown that the MIDL provides significant gains in terms of computational efficiency and imaging performance of PCI lithography system.