A Comparison of Optimization Methods for the Transparent Conducting Oxide Application of Ga-doped ZnO

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Hyun-Soo Kim and Sang-Gyu Lee and Seung-Soo Han and 
                 Hyeon Bae and Tae-Ryong Jeon and Sungshin Kim",
  title =        "A Comparison of Optimization Methods for the
                 Transparent Conducting Oxide Application of {Ga}-doped
  booktitle =    "Fourth International Conference on Natural
                 Computation, ICNC '08",
  year =         "2008",
  month =        oct,
  volume =       "1",
  pages =        "126--130",
  keywords =     "genetic algorithms, genetic programming, error
                 back-propagation algorithm, fractional factorial
                 design, neural networks, optimal process conditions,
                 optimization methods, particle swarm optimization,
                 transparent conducting oxide, backpropagation,
                 dielectric thin films, electrical engineering
                 computing, gallium, neural nets, particle swarm
                 optimisation, zinc compounds",
  DOI =          "doi:10.1109/ICNC.2008.806",
  abstract =     "In this paper, statistical experimental design is used
                 to characterize the transparent conducting oxide
                 process of Ga-doped ZnO. Fractional factorial design
                 with three center points are employed. In the process
                 modeling, neural networks trained by the error
                 back-propagation algorithm and genetic programming are
                 applied to map the relationships between several input
                 factors and resistivity. Both modeling methods are
                 typical modeling methods for local and global
                 approaches. Subsequently, both genetic algorithms and
                 particle swarm optimization are used to identify the
                 optimal process conditions to minimize resistivity. The
                 results of the two approaches are compared, and the
                 optimized resistivity found by the particle swarm
                 method was slightly better than that found by genetic
                 algorithms. More importantly, repeated applications of
                 particle swarm optimization yielded process conditions
                 with smaller standard deviations, implying greater
                 consistency in recipe generation.",
  notes =        "Also known as \cite{4666824}",

Genetic Programming entries for Hyun-Soo Kim Sang-Gyu Lee Seung-Soo Han Hyeon Bae Tae-Ryong Jeon Sungshin Kim