Inverse Lithography Physics-informed Deep Neural Level Set for Mask
  Optimization
Xing-Yu Ma Shaogang Hao
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.