Optical proximity correction (OPC) is a vital step to ensure printability in
modern VLSI manufacturing. Various OPC approaches based on machine learning
have been proposed to pursue performance and efficiency, which are typically
data-driven and hardly involve any particular considerations of the OPC
problem, leading to potential performance or efficiency bottlenecks. In this
paper, we propose CAMO, a reinforcement learning-based OPC system that
specifically integrates important principles of the OPC problem. CAMO
explicitly involves the spatial correlation among the movements of neighboring
segments and an OPC-inspired modulation for movement action selection.
Experiments are conducted on both via layer patterns and metal layer patterns.
The results demonstrate that CAMO outperforms state-of-the-art OPC engines from
both academia and industry.

基于强化学习的 CAMO 光刻接近性校正系统结合了相邻片段移动的空间相关性和校正启发式变调选择，对比学术界和工业界最先进的光刻接近性校正引擎，实验证明 CAMO 的性能优于其它引擎。

CAMO: 相关感知遮罩优化与调制增强学习

CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement  Learning

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.

基于水平集的逆向光刻技术在深度学习框架内实现掩膜优化，显著提高印刷性能，减少计算时间，为逆向光刻领域提供了一种高效的解决方案。

反向曝光实验物理驱动的深度神经水平集掩膜优化

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

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。