Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling

• Published in: Soft computing (Berlin, Germany) Germany), 2010, Vol.14 (2), p.161-169
• Main Authors: Bae, Hyeon, Jeon, Tae-Ryong, Kim, Sungshin, Kim, Hyun-Soo, Kim, Dongseop, Han, Seung-Soo, May, Gary S.
• Format: Article
• Language: English
• Subjects: Acids; Artificial Intelligence; Back propagation; Computational Intelligence; Control; Design; Efficiency; Electrical resistivity; Emitters; Engineering; Focus; Genetic algorithms; Manufacturing; Mathematical Logic and Foundations; Mathematical models; Mechatronics; Methods; Modelling; Neural networks; Neurons; Nitrogen; Optimization; Particle swarm optimization; Photovoltaic cells; R&D; Reflectance; Research & development; Robotics; Screen printing; Silicon nitride; Silicon wafers; Solar cells; Statistical analysis; Temperature; Texturing
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-009-0438-9

This study describes techniques for the cascade modeling and the optimization that are required to conduct the simulator-based process optimization of solar cell fabrication. Two modeling approaches, neural networks and genetic programming, are employed to model the crucial relation for the consecutively connected two processes in solar cell fabrication. One model (Model 1) is used to map the five inputs (time, amount of nitrogen and DI water in surface texturing and temperature and time in emitter diffusion) to the two outputs (reflectance and sheet resistance) of the first process. The other model (Model 2) is used to connect the two inputs (reflectance and sheet resistance) to the one output (efficiency) of the second process. After modeling of the two processes, genetic algorithms and particle swarm optimization were applied to search for the optimal recipe. In the first optimization stage, we searched for the optimal reflectance and sheet resistance that can provide the best efficiency in the fabrication process. The optimized reflectance and sheet resistance found by the particle swarm optimization were better than those found by the genetic algorithm. In the second optimization stage, the five input parameters were searched by using the reflectance and sheet resistance values obtained in the first stage. The found five variables such as the texturing time, amount of nitrogen, DI water, diffusion time, and temperature are used as a recipe for the solar cell fabrication. The amount of nitrogen, DI water, and diffusion time in the optimized recipes showed considerable differences according to the modeling approaches. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller variations, implying greater consistency in recipe generation.