Prediction of thin film thickness of field emission using wavelet neural networks

• Published in: Thin solid films Vol. 473; no. 2; pp. 224 - 229
• Main Authors: Cui, Wan-zhao, Zhu, Chang-chun, Zhao, Hong-po
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 14.02.2005; Elsevier Science
• Subjects: Computer simulation; Condensed matter: electronic structure, electrical, magnetic, and optical properties; Condensed matter: structure, mechanical and thermal properties; Electron and ion emission by liquids and solids; impact phenomena; Electronic structure and electrical properties of surfaces, interfaces, thin films and low-dimensional structures; Exact sciences and technology; Field emission; Field emission, ionization, evaporation, and desorption; Physics; Surfaces and interfaces; thin films and whiskers (structure and nonelectronic properties); Thin films; Wavelet neural networks; Thin films; Field emission; 73.61.-r; Computer simulation; Wavelet neural networks; 79.70.+q; Wavelet transforms; Diamonds; Computerized simulation; Electron field emission; Thickness; Errors; Neural networks; 79.70.+q Thin films
• ISSN: 0040-6090; 1879-2731
• DOI: 10.1016/j.tsf.2004.06.121

Based on wavelet transforms extracting characteristic features from experimental data, the wavelet neural network (WNN) is used as an elementary model to study the characteristics of field emission from thin films. The WNN model is trained with learning samples of thin film thickness. The function mappings that the trained WNN model contains are the very ones that thin film thickness varies with characteristic parameters of field emission. A predicting model on thin film thickness of field emission is obtained. The data of thickness of diamond thin films is used to test this model. The results show that the absolute value of the relative error is within 2.98%, and the well-trained WNN model possesses good forecasting characteristics.