Artificial bee colony algorithm for small signal model parameter extraction of MESFET

This paper presents an application of swarm intelligence technique namely Artificial Bee Colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs Metal Extended Semiconductor Field Effect Transistor (MESFET) device and compares its performance with Particle Swarm Optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the Quality of Solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16 element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5 to 25 GHz.