High-precision lithography thick-mask model based on a decomposition machine learning method

Abstract

The thick-mask model had been used to simulate the diffraction behavior of the three-dimensional photomask in optical lithography system. By exploring the edge interference effect that appears in the diffraction near-field (DNF), an improved thick-mask model with high precision is proposed. The diffraction transfer matrix (DTM) is introduced to represent the transformation from the layout pattern to the corresponding DNF. In this method, the DTM is learned from a training library including the rigorous DNF of some representative mask clips. Given a thick-mask pattern, it is firstly decomposed into a set of segments around the sampling points at corners and edges. Then, the local DNF of each segment is calculated based on the corresponding DTM. Finally, all the local DNF segments are synthesized together to simulate the entire thick-mask DNF. The results show that the proposed method can significantly improve the simulation accuracy compared to the traditional filter-based method, meanwhile retaining a high computation speed.