Parameter extraction for PSP MOSFET model using hierarchical particle swarm optimization
The particle swarm optimization (PSO) algorithm is applied to the problem of MOSFET parameter extraction for the first time. It is shown to perform significantly better than the genetic algorithm (GA). Several modifications of the basic PSO algorithm have been implemented: (a) Hierarchical PSO (HPSO) in which particles are hierarchically arranged and influenced by the positions of the local and global leaders, (b) memory loss operation due to which a particle forgets its past best position, (c) intensive local search in which the solution space around the global leader is searched with a high resolution, and (d) adaptive inertia which causes the inertia of the particles to change adaptively, depending on the fitness of the population. It is demonstrated that the above features improve the performance of the basic PSO algorithm both for the MOSFET parameter extraction problem and for benchmark functions.
psp mosfet model hierarchical particle swarm optimization particle swarm optimization mosfet parameter extraction genetic algorithm memory loss local search adaptive inertia