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

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@Article{journals/soco/BaeJKKKHM10,
  title =        "Optimization of silicon solar cell fabrication based
                 on neural network and genetic programming modeling",
  author =       "Hyeon Bae and Tae-Ryong Jeon and Sungshin Kim and 
                 Hyun-Soo Kim and DongSeop Kim and Seung Soo Han and 
                 Gary S. May",
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "2010",
  number =       "2",
  volume =       "14",
  pages =        "161--169",
  keywords =     "genetic algorithms, genetic programming, Neural
                 network, Particle swarm optimization, Silicon solar
                 cell fabrication",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-009-0438-9",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/soco/soco14.html#BaeJKKKHM10",
  abstract =     "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.",
}

Genetic Programming entries for Hyeon Bae Tae-Ryong Jeon Sungshin Kim Hyun-Soo Kim DongSeop Kim Seung-Soo Han Gary S May

Citations