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.

翻译：随着集成电路特征尺寸持续微缩，光学邻近效应校正（OPC）作为光刻工艺中确保高可印性的关键分辨率增强技术，已引起广泛关注。近年来，基于水平集的逆光刻技术（ILT）因其在先进工艺中展现的卓越图案保真度，成为极具前景的OPC解决方案。然而，ILT巨大的计算耗时严重限制了其应用范围，主要局限于校正部分层与热点区域。深度学习方法在加速ILT方面展现出巨大潜力，但缺乏逆光刻领域知识制约了基于深度学习的算法在工艺窗口增强等方面的能力。本文提出一种逆光刻物理信息深度神经水平集（ILDLS）掩模优化方法。该方法将基于水平集的ILT作为深度学习框架中的一层，通过迭代执行掩模预测与校正，相较于纯深度学习和ILT结果，显著提升了可印性与工艺窗口。与ILT相比，计算时间降低了数个数量级。通过将逆光刻物理知识融入深度学习，ILDLS提供了一种新型高效的掩模优化解决方案。