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