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).

翻译：随着先进工艺节点中特征尺寸的持续缩小，掩模优化在传统设计流程中愈发关键，同时光学邻近效应校正（OPC）方法也伴随着计算开销的爆炸性增长。近年来，逆光刻技术（ILT）受到广泛关注并在新兴OPC解决方案中逐渐占据主导地位。然而，现有ILT方法或存在耗时问题，或在掩模可印性与可制造性方面表现欠佳。本文提出DevelSet框架——一种基于GPU和深度神经网络（DNN）加速的金属层水平集OPC框架。我们首先通过引入曲率项降低掩模复杂度，并运用GPU加速克服计算瓶颈，改进了传统基于水平集的ILT算法。为进一步提升可印性与快速迭代收敛性能，我们创新性地设计了一种深度融合水平集内在原理的深度神经网络，以促进DNN与GPU加速水平集优化器的联合优化。实验结果表明，DevelSet框架在可印性方面超越现有最优方法，并将运行效率提升至即时级别（约1秒）。