Multi-Objective Prediction Model and Parameter Optimization Model for the Sputtering of Aluminum Zinc Oxide Semiconducting Transparent Thin Films

作者：Yang Ching Been^*; Chiang Hsiu Lu; Hsu Chun Yao

来源：Journal of Computational and Theoretical Nanoscience, 2013, 10(9): 2056-2065.

DOI：10.1166/jctn.2013.3168

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

The industry demand for both high conductivity and transmittance in thin films has made it essential to develop a multi-objective prediction model for resistivity and transmittance. This study combined Taguchi methods and artificial neural networks (ANN) to construct a multi-objective prediction model for the sputtering of AZO (ZnO:Al = 97:3 wt%) to produce semiconducting transparent thin films. The Levenberg-Marquardt method was incorporated into the multi-objective prediction model to construct a multi-objective parameter optimization model for AZO semiconducting transparent thin films. The squared difference of the objective values and the predicted values of each objective served as the error function, which was then multiplied by the individual weight values and summed to derive the objective function of the system. In conjunction with the Levenberg-Marquardt method and reasonable convergence criteria, the optimal combination of parameters for the sputtering objectives was obtained. These parameters included radio frequency power (R. F. power) power of 120 W, process pressure of 15 mTorr, film thickness of 300 nm, and substrate temperature of 74 degrees C. The objective resistivity was 11.4 x 10(-3) Omega . cm, and the objective transmittance was 88.9%. In this experiment, resistivity resulted in 10.6 x 10(-3) Omega . cm, with an error of 7.5% between the predicted value and the experiment results. Transmittance reached 89.1% in the experiment, accounting for an error of -0.2%.

• 出版日期2013-9