Inverse Lithography Physics-informed Deep Neural Level Set for Mask Optimization

TL;DR

This paper introduces a physics-informed deep neural level set method for mask optimization in lithography, significantly reducing computation time while improving printability and process window compared to traditional inverse lithography techniques.

Contribution

It integrates level set-based inverse lithography into a deep learning framework, enhancing efficiency and accuracy in mask optimization.

Findings

Computation time reduced by orders of magnitude.

Improved printability and process window over pure DL and ILT.

Effective mask optimization through physics-informed deep learning.

Abstract

As the feature size of integrated circuits continues to decrease, optical proximity correction (OPC) has emerged as a crucial resolution enhancement technology for ensuring high printability in the lithography process. Recently, level set-based inverse lithography technology (ILT) has drawn considerable attention as a promising OPC solution, showcasing its powerful pattern fidelity, especially in advanced process. However, massive computational time consumption of ILT limits its applicability to mainly correcting partial layers and hotspot regions. Deep learning (DL) methods have shown great potential in accelerating ILT. However, lack of domain knowledge of inverse lithography limits the ability of DL-based algorithms in process window (PW) enhancement and etc. In this paper, we propose an inverse lithography physics-informed deep neural level set (ILDLS) approach for mask optimization. This approach utilizes level set based-ILT as a layer within the DL framework and iteratively conducts mask prediction and correction to significantly enhance printability and PW in comparison with results from pure DL and ILT. With this approach, computation time is reduced by a few orders of magnitude versus ILT. By gearing up DL with knowledge of inverse lithography physics, ILDLS provides a new and efficient mask optimization solution.