Abstract
We present, in this paper, an intelligent extraction technique for obtaining a set of optimal model parameters of the Berkeley short-channel insulated gate field effect transistor model 4 (BSIM4) for sub-100 nm metal-oxide-semiconductor field effect transistors (MOSFETs). Based on the genetic algorithm (GA), the monotone iterative Levenberg-Marquardt (MI-LM) method, and the neural network (NN) algorithm, this novel approach can perform simultaneous BSIM4 parameter extraction with more than 16 sub-100 nm MOSFETs in a global sense. Before extraction, all input measured I-V data are preprocessed by statistical reduction and sampling procedures. The GA and MI-LM method are then applied to calculate all parameters. The NN algorithm is used to trace the errors of I-V curves and their first derivatives, and also to inspect the variations of physical quantities. Once parameters are found, the postprocess will identify a sensible searching path so that the solution engine continues evolution until parameters reach specified stopping criteria. Good accuracy is obtained for the 90 nm NMOSFETs by several testing cases. Compared with manual fitting processes, this methodology overcomes the conventional inconvenience and is cost-effective in automatic parameter extraction. It bridges device fabrication technology and system-on-a-chip (SOC) design.