Combining the taguchi method with an artificial neural network to construct a multi-target prediction model for aluminum zinc oxide semiconducting transparent thin film

Journal of Computational and Theoretical Nanoscience

Volumn 9, Issue 12, 2012, Pages 2027-2036

  Yang, Ching Been ^a   Hsu, Hsin Yun ^b   Chen, Chun Sheng ^c  

^a TAOYUAN INNOVATION INSTITUTE OF TECHNOLOGY   (Taiwan)

^b NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

^c LUNGHWA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Artificial Neural Network; AZO (ZnO:Al = 97:3 wt ); Resistivity; Semiconducting Transparent Thin Film; Transmittance

Indexed keywords

ALUMINUM ZINC OXIDES; DEPOSITION PARAMETERS; GLOBAL PREDICTIONS; RADIO FREQUENCIES; SUBSTRATE TEMPERATURE; TRANSMITTANCE; TRANSPARENT THIN FILM; ZNO:AL;

ALUMINUM; ELECTRIC CONDUCTIVITY; FILM THICKNESS; MATHEMATICAL MODELS; NEURAL NETWORKS; SUBSTRATES; TAGUCHI METHODS; THIN FILMS; VAPOR DEPOSITION; ZINC OXIDE;

DEPOSITION;

EID: 84876591530     PISSN: 15461955     EISSN: 15461963     Source Type: Journal    
DOI: 10.1166/jctn.2012.2610     Document Type: Article

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