Parallel genetic algorithm for intelligent model parameter extraction of metal-oxide-semiconductor field effect transistors

Equivalent circuit model of semiconductor devices associated with a set of optimized parameters currently plays a central role in the circuit design and semiconductor manufacturing communities. An intelligent model parameter extraction system that simultaneously integrates evolutionary and numerical optimization techniques for optimal characterization of sub-100nm metal-oxide-semiconductor field effect transistors (MOSFETs) has recently been advanced [1]. In this article, to accelerate the extraction process, parallelization of the genetic algorithm (GA) for the intelligent model parameter extraction system of MOSFETs is developed. The GA implemented in the extraction system is mainly parallelized with a diffusion scheme on a PC-based Linux cluster with message passing interface libraries. Parallelization of GA is governed by various factors, which affect the quality of extracted parameters and its computational efficiency. The result obtained in this study shows that the diffusion GA is superior to an isolated GA, and the superiority of the diffusion GA becomes significant when the number of MOSFETs to be optimized is increased. Theoretical estimation and preliminary numerical implementation of parallel GA show that there exist an optimal number of processors with respect to the number of devices to be extracted. Benchmark results, such as speedup and efficiency including accuracy of extraction are presented and discussed for different sets of realistic multiple sub-100nm devices to show the robustness and efficiency of the method. Practical implementation of the parallel GA approach benefits the engineering of device model parameter extraction in nowadays semiconductor manufacturing industry.