A Novel Optimization Algorithm for Deep-submicron MOSFET EKV Model Parameters Extraction

Various compact models have been of great interest and studied for deep-submicron metal-oxide-semiconductor field effect transistor (MOSFET) device simulation. The model parameters extraction intrinsically characterizes properties of designed and fabricated devices. It leads to a multidimensional optimization problem to be solved and extracted efficiently for the applications to very large scaled integrated (VLSI) circuit and system-on-a-chip (SoC) design. Different approaches have been proposed to solve this problem. However, these approaches encounter serious problems in deep-submicron MOSFET era, such as poor accuracy, time-consuming, ineffective extraction process, and lack the predict capacity in practical applications. In this work we present an intelligent hybrid system for EKV model in deep-submicron MOSFET model parameters extraction. This approach combines with the genetic algorithm (GA) and the numerical optimization, has been proposed and successfully developed. Excellent accuracy has been obtained in several experiments for both the 0.18 μm and 0.25 μm NMOSFET devices in terms of IDS-VDS and IDS-VGS curves. Comparing to conventional approaches, the proposed methodology contained the advantage of both numerical and soft computing methods solves the extracting problem cost efficiently. The proposed extraction architecture significantly reduces the demand of time and enhances the working performance in the practice use, which is useful in VLSI circuit and SoC design. Key-Words: MOSFET, EKV model, parameters extraction, genetic algorithm, Levenberg-Marquardt method