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

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.