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

被引：20

作者：

Kim, Chang Eun ^{[1
]}

Moon, Pyung ^{[1
]}

Yun, Ilgu ^{[1
]}

Kim, Sungyeon ^{[2
]}

Myoung, Jae-Min ^{[2
]}

Jang, Hyeon Woo ^{[3
]}

Bang, Jungsik ^{[3
]}

机构：

[1] Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea

[2] Yonsei Univ, Dept Mat Sci & Engn, Seoul 120749, South Korea

[3] LG Chem Ltd, Taejon 305380, South Korea

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 03期

关键词：

Ga-doped zinc oxide; Transparent conductive oxide; Figure of merit; Neural networks; Genetic algorithm; Optimization; DOPED ZNO; TEMPERATURE; DEPOSITION;

D O I：

10.1016/j.eswa.2010.08.074

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：2823 / 2827

页数：5

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