Application of back-propagation artificial neural network (ANN) to predict crystallite size and band gap energy of ZnO quantum dots

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

AIP Conference Proceedings

Volume

1901

Publication Date

12-4-2017

Abstract

Herein, the crystallite size and band gap energy of zinc oxide (ZnO) quantum dots were predicted using artificial neural network (ANN). Three input factors including reagent ratio, growth time, and growth temperature were examined with respect to crystallite size and band gap energy as response factors. The generated results from neural network model were then compared with the experimental results. Experimental crystallite size and band gap energy of ZnO quantum dots were measured from TEM images and absorbance spectra, respectively. The Levenberg-Marquardt (LM) algorithm was used as the learning algorithm for the ANN model. The performance of the ANN model was then assessed through mean square error (MSE) and regression values. Based on the results, the ANN modelling results are in good agreement with the experimental data. © 2017 Author(s).

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Digitial Object Identifier (DOI)

10.1063/1.5010451

Disciplines

Electrical and Electronics | Mining Engineering

Keywords

Zinc oxide

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