A Floating-Point Based Evolutionary Algorithm for Model Parameters Extraction and Optimization in HBT Device Simulation

Abstract

In this paper, we extract the HBT model parameters with a computational intelligence algorithm for the optimal VLSI device characterization. For a specified VLSI circuit, the proposed method to solve the HBT equivalent model and to extract parameters is based on the monotone iterative (MI) method and the genetic algorithm (GA) with floating-point operators. First, a set of nonlinear equations is solved with the MI method, and the solved results are used for the optimization with the GA method. The iteration will be stopped when the self-consistent convergent solution is obtained. Our simulation results demonstrate this method has excellent convergent property and highly computational efficiency. The approach not only provides a novel alternative for optimal VLSI circuit and device design but also has many practical applications in nanodevice I-V characterization, RF circuit optimization and system-on-a-chip function design.