Application of a revised multi-objective genetic algorithm to parameters optimization of a solar cell manufacturing process

Diffusion is one of the core processes of a solar cell manufacturing. It is used to produce the p-n junction with an expected sheet resistance and with the minimum sheet resistance standard deviation. This is a multiple quality criteria optimization problem. In order to find the optimal process parameters for the silicon solar cell diffusion process, this research proposed two new approaches, a revised multi-objective genetic algorithms (RMOGA) and an adaptive multi-objective genetic algorithms (AMOGA), which both integrated back-propagation neural networks (BPN), technique for order preference by similarity to ideal solution (TOPSIS), and genetic algorithms (GA) with the concept of elite sets and local search. The result of this study shows that AMOGA has the best performance to enhance the breadth and depth of the MOGA search, and also quickly converges to the optimal solutions.