粒子群算法在薄膜参数反演中的参数选择

摘要

The standard particle swarm optimization is used to guarantee the convergent global optima of the PSO which is applied to the inverting of ellipsometry. The linear relationship of kci + c2 = d is applied to the parameters of c and cz. With different a/s selected, the optimal performance of the PSO for inverting the optical constant of film SiC^/Si was analyzed by numerical experiment . The result shows that,with the inertia weight u> ranging from 0. 5 to 0. 8, the sum of learning parameters ciand cz preferably is not more than 3, and that matching the smaller c with the larger cz guarantees the better optimization performance of the PSO.%为确保用于薄膜椭偏参数反演计算的粒子群算法收敛于全局最优值,采用标准粒子群算法,学习参数c1和c2采用线性关系kc1+c2=d,选取不同惯性系数ω,通过数值实验分析算法反演Si基底上SiO2薄膜光学常数的优化性能,结果表明粒子群算法的惯性系数ω取值范围在0.5～0.8之间,学习参数c1,c2配对之和不超过3,较小的c1与较大的c2相配对,可以保证用于椭偏参数反演的粒子群算法具有较好的优化性能.