With the feature size continuously shrinking in advanced technology nodes,
mask optimization is increasingly crucial in the conventional design flow,
accompanied by an explosive growth in prohibitive computational overhead in
optical proximity correction (OPC) methods. Recently, inverse lithography
technique (ILT) has drawn significant attention and is becoming prevalent in
emerging OPC solutions. However, ILT methods are either time-consuming or in
weak performance of mask printability and manufacturability. In this paper, we
present DevelSet, a GPU and deep neural network (DNN) accelerated level set OPC
framework for metal layer. We first improve the conventional level set-based
ILT algorithm by introducing the curvature term to reduce mask complexity and
applying GPU acceleration to overcome computational bottlenecks. To further
enhance printability and fast iterative convergence, we propose a novel deep
neural network delicately designed with level set intrinsic principles to
facilitate the joint optimization of DNN and GPU accelerated level set
optimizer. Experimental results show that DevelSet framework surpasses the
state-of-the-art methods in printability and boost the runtime performance
achieving instant level (around 1 second).

本文提出了 DevelSet 框架，它是一种 GPU 加速的、基于深度神经网络的水平集 OPC 框架，可用于金属层的光刻，相比于现有技术，在适性和运行时间方面有显著提升。