Hybrid Differential Evolution and Particle Swarm Optimization Approach to Surface-Potential-Based Model Parameter Extraction for Nanoscale MOSFETs

Abstract

A set of semiconductor device model and parameters bridges the communities between circuit design and chip fabrication. In this article, we present an intelligent extraction technique for obtaining a set of optimal model parameters of the surface-potential-based PSP model for the sub-45-nm metal-oxide-semiconductor field effect transistors (MOSFETs). The proposed algorithm combines the standard velocity and position update rules in a particle swarm optimization (PSO) algorithm, and the operations of differential mutation and probability crossover from a differential evolution method. This differential approach can increase the diversity of the population and help particles escape from the local optimal solutions. In addition, the adopted fitness function considers not only the error of the I-V curves, but also their first derivatives. Compared with conventional engineering extraction strategy, the hybrid method extracts 14 DC parameters simultaneously for sub-45-nm N-MOSFET devices. The best accuracy and interesting computational efficiency are obtained by several testing cases.