基于半隐式离散化的局部水平集掩模优化

Local Level Set Based Mask Optimization with Semi-Implicit Discretization

提出一种面向光刻掩模优化框架基于半隐式离散化的数值技术方法,对框架中稳定时间相关模型中的扩散项、非扩散项分别进行隐式、显式的离散化,从而克服基于梯度下降的显式离散化方法中迭代步长受到抑制的稳定性约束要求。此外,选择对掩模图形的边缘与高频成分相对应的受监控像素点进行局部优化,而不是优化所有的掩模像素点,来降低计算复杂度。仿真结果显示,所提掩模优化算法在降低优化维度的同时提高了优化收敛效率。

This paper proposes to apply a semi-implicit difference scheme where diffusion terms in the time-dependent partial differential equation are discretized implicitly and non-diffusion ones explicitly thus overcoming the prohibitive step-size in lithographic mask optimization(MO).Further,monitoring pixels on mask pattern contour instead of all pattern pixels are selected locally corresponding to high-frequency layout component and optimized to ease the computation complexity.Superior MO performance is demonstrated by the simulation results in terms of improved convergence with reduced optimization dimensionality.