A comparison and analysis of genetic algorithm and particle swarm optimization using neural network models for high efficiency solar cell fabrication processes

In this paper, statistical experimental design is used to characterize the surface texturing and emitter diffusion formation processes for high-performance silicon solar cells. The output characteristics considered are reflectance, sheet resistance, diffusion depth, and cell efficiency. The influence of each parameters affected to efficiency is investigated through the main effect and interaction analysis. Sequential neural network process models are constructed to characterize the entire 3-step process. In the sequential scheme, each work cell sub-process is modeled individually, and each sub-process model is linked to previous sub-process outputs and subsequent sub-process inputs. These neural network models are used for process optimization using both genetic algorithms and particle swarm optimization to maximize cell efficiency. The optimized efficiency found via particle swarm optimization showed better performance than optimized efficiency found via genetic algorithms.