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As the feature size of integrated circuits continues to decrease, opticalproximity correction (OPC) has emerged as a crucial resolution enhancementtechnology for ensuring high printability in the lithography process. Recently,level set-based inverse lithography technology (ILT) has drawn considerableattention as a promising OPC solution, showcasing its powerful patternfidelity, especially in advanced process. However, massive computational timeconsumption of ILT limits its applicability to mainly correcting partial layersand hotspot regions. Deep learning (DL) methods have shown great potential inaccelerating ILT. However, lack of domain knowledge of inverse lithographylimits the ability of DL-based algorithms in process window (PW) enhancementand etc. In this paper, we propose an inverse lithography physics-informed deepneural level set (ILDLS) approach for mask optimization. This approach utilizeslevel set based-ILT as a layer within the DL framework and iteratively conductsmask prediction and correction to significantly enhance printability and PW incomparison with results from pure DL and ILT. With this approach, computationtime is reduced by a few orders of magnitude versus ILT. By gearing up DL withknowledge of inverse lithography physics, ILDLS provides a new and efficientmask optimization solution.

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