基于粒子群优化算法的光刻机光源优化方法

Source Optimization Using Particle Swarm Optimization Algorithm in Optical Lithography

提出了一种基于粒子群优化算法的光刻机光源优化方法。将光源信息编码为粒子,利用图形误差作为评价函数,通过更新粒子的速度与位置信息不断迭代优化光源图形。对周期接触孔阵列和含有交叉门的复杂掩模图形的仿真验证表明,两者的图形误差分别降低了66.1%和27.3%,有效提高了光刻成像质量。与基于遗传算法的光源优化方法相比,该方法具有更快的收敛速度。另外,还研究了像差和离焦对本方法稳健性的影响。

An efficient source optimization method using particle swarm optimization algorithm is proposed. The fidelity is adopted as the fitness function. Sources are encoded into particles, and then optimization is implemented by updating the velocities and positions of these particles. This method is demonstrated by using two typical mask patterns, including a periodic array of contact holes and a complex pattern with cross gate design. The pattern errors are reduced by 66.1% and 27.3%, respectively. The results show that the proposed method leads to faster convergence than the source optimization method using genetic algorithm while improving the image quality at the same time.The robustness of the proposed method is also verified by adding aberrations and defocus respectively.