Particle swarm approach for parameter optimization of quantum well nano structure

Analytical optimization techniques suffer from slow convergence in complex solution space. Heuristics-based swarm intelligence is an efficient alternative to analytical optimization techniques. In this paper, particle swarm optimization approach is utilized for better and efficient nano-device modeling. Mobility of two-dimensional hot electrons in modulation doped square quantum well of AlGaAs/GaAs which is determined using heated drifted Fermi–Dirac distribution function and relevant scattering mechanisms is taken as the fitness/objective function. The 2D carrier concentration, quantum well width and lattice temperature of the quantum well are taken as the input variables. The algorithm with three input variables is then utilized to get optimized values of input parameters to get desired ac and dc mobility values.