Process estimation and optimized recipes of ZnO:Ga thin film characteristics for transparent electrode applications

Chang Eun Kim, Pyung Moon, Ilgu Yun, Sungyeon Kim, Jae Min Myoung, Hyeon Woo Jang, Jungsik Bang

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Ga-doped zinc oxide (ZnO:Ga) thin films were prepared on glass substrate by magnetron sputtering at room temperature (RT) and thermally annealed in hydrogen atmosphere for 1 h. The effects of film thickness and annealing temperature on sheet resistance, transmittance and figure of merit of ZnO:Ga thin films were analyzed and modeled using the artificial neural networks (NNets). The NNet models presented the good prediction on sheet resistance, transmittance and figure of merit of ZnO:Ga thin films and it was found that the electrical and optical properties of ZnO:Ga thin films were enhanced by thermal annealing. After NNet models were verified, genetic algorithm (GA) was used to search the optimized recipe for the desired figure of merit of ZnO:Ga thin films. The methodology allows us to estimate the optimal process condition with a small number of experiments.

Original languageEnglish
Pages (from-to)2823-2827
Number of pages5
JournalExpert Systems with Applications
Volume38
Issue number3
DOIs
Publication statusPublished - 2011 Mar 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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