An Evolutionary Approach Based Optimization of Small Signal Parameters for GSDG MOSFET

This paper present the optimization of small- signal parameters for GSDG (Gate Stack Double Gate) MOSFET in deep submicron analogue circuit design. The evolutionary optimization algorithms taken for this study are CRPSO (Craziness-based Particle Swarm Optimization) and ALCPSO (Aging Leader Challenger based PSO). The small-signal parameters like trans-conductance, OFF-state current and different other design parameters of GSDG MOSFET are optimized in the regions of sub-threshold and saturation from the above mentioned evolutionary techniques to achieve the superior electrical performance of the MOSFET in the analogue domain. The results obtained from ALCPSO and CRPSO techniques are much improved as compared with the results of preceding literature and may be considered for the useful device design.