基于自适应非线性粒子群算法的光刻光源优化方法

Source optimization based on adaptive nonlinear particle swarm method in lithography

光刻光源优化作为必不可少的分辨率增强技术之一,能够提高先进光刻成像质量。在先进光刻领域,光源优化的收敛效率和优化能力是至关重要的。粒子群优化算法作为一种全局优化算法,自适应控制策略可以提高粒子的全局搜索能力,非线性控制策略可以扩大粒子搜索范围。本文提出一种基于自适应非线性控制策略的粒子群优化算法,将光刻光源优化问题转换成多变量评价函数求解。对简单周期光栅图形和不规则图形进行成像优化仿真,通过粒子群优化算法的全局迭代特性优化光源形貌。利用图形误差(PEs)作为多变量评价函数,对迭代300次的仿真结果进行评价,两种仿真图形的PEs分别降低52.2%和35%。与传统粒子群优化算法和遗传算法相比,该方法不仅能提高成像质量,而且具有更高的收敛效率。

As an essential resolution enhancement technique,source optimization can improve the quality of ad-vanced lithography.In the field of advanced lithography,the convergence efficiency and optimization ability of the source optimization are very important.Particle swarm optimization(PSO)is a global optimization algorithm.The adaptive control strategy can improve the global search ability of particles,and the nonlinear control strategy can expand the search range of particles.In this paper,a PSO algorithm based on adaptive nonlinear control strategy(ANCS)is proposed to solve the problem of source optimization by transforming it into a multivariable evaluation function.The image optimization simulation is carried out with a brief periodic grating image and an irregular image,and the source shape is optimized by the global iteration property of the proposed method.By using the pattern er-rors(PEs)as a multivariate merit function,the results of 300 iterations are evaluated,and the PEs of the two kinds of simulation patterns are reduced by 52.2%and 35%,respectively.Compared with the traditional PSO algorithm and genetic algorithm,the proposed method not only improves the imaging quality,but also has higher convergence ef-ficiency.