Applying theoretical spectra to artificial neural networks for real-time estimation of thin film thickness

Tzong-Daw Wu; Jiun-Shen Chen; Ching-Pei Tseng; Cheng-Chang Hsieh

doi:10.1117/1.OE.55.12.125106

22 December 2016 Applying theoretical spectra to artificial neural networks for real-time estimation of thin film thickness

Tzong-Daw Wu, Jiun-Shen Chen, Ching-Pei Tseng, Cheng-Chang Hsieh

Author Affiliations +

Optical Engineering, Vol. 55, Issue 12, 125106 (December 2016). https://doi.org/10.1117/1.OE.55.12.125106

Abstract

This paper presents the application of backpropagation neural networks (BPNNs) for estimating the thickness of deposited silver (Ag) films on polyethylene terephthalate substrate via a roll-to-roll magnetron sputtering system. The thickness of thin films affects the optical properties of films, while the transmittance implies the thickness. Nevertheless, thin films are unlike their bulk counterparts whose absorptions of light are proportional to their thicknesses. Moreover, the interference is considerable. Thus, BPNNs are applied for estimating thickness of Ag films. BPNNs were trained via theoretical transmittance spectra because they can be quickly generated and reduce actual experiments. The BPNNs were applied to estimate thickness via actual spectra. Different levels of noise were also added to the theoretical spectra to improve the performance of BPNNs. The results show that the estimation of BPNNs is more accurate when adding slight noise to the theoretical spectra. The average error is ∼0.027 when 3% noise is added to the training spectra, while the error of spectra without adding noise is greater than 0.12.

Citation Download Citation

Tzong-Daw Wu, Jiun-Shen Chen, Ching-Pei Tseng, and Cheng-Chang Hsieh "Applying theoretical spectra to artificial neural networks for real-time estimation of thin film thickness," Optical Engineering 55(12), 125106 (22 December 2016). https://doi.org/10.1117/1.OE.55.12.125106

Received: 22 July 2016; Accepted: 30 November 2016; Published: 22 December 2016

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