Abstract
This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.
Publisher
Trans Tech Publications, Ltd.
Subject
Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science
Reference11 articles.
1. K. Ali, S.A. Khan, and M.Z.M. Jafri: Effect of double layer (SiO2∕TiO2) anti-reflective coating on silicon solar cells. Int. J. Electrochem. Sci. 9 (Oct. 2014), 7865-7874.
2. A.A. Solovyev, S.V. Rabotkin, and N.F. Kovsharov: Polymer films with multilayer low-E coatings. Mater. Sci. Semicond. Process. 38 (Oct. 2015), 373-380.
3. M. Ylilammi, and T. Ranta-aho: Optical determination of the film thicknesses in multilayer thin film structures. Thin Solid Films 232 (Sep. 1993), 56-62.
4. J.C. Manifacier, J. Gasiot, and J.P. Fillard: A simple method for the determination of the optical constants n, k and the thickness of a weakly absorbing thin film. J. Phys. E: Sci. Instrum. 9, 11 (1976), 1002-1004.
5. Y.D. Pyung, M. Chang, and E. Kim, et al: Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms. Expert Syst. Appl. 36, 2 (Mar. 2009), 4061-4066.