Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/13765
Title: Enhancing thickness determination of nanoscale dielectric films in phase diffraction-based optical characterization systems with radial basis function neural networks
Authors: Ataç, Enes
Karatay, Anıl
Dinleyici, Mehmet Salih
Keywords: Phase diffraction
Neural networks
Optical fiber sensors
Optical characterization
Issue Date: 2023
Publisher: IOP Publishing
Abstract: Accurate determination of the optical properties of ultra-thin dielectric films is an essential and challenging task in optical fiber sensor systems. However, nanoscale thickness identification of these films may be laborious due to insufficient and protracted classical curve matching algorithms. Therefore, this experimental study presents an application of a radial basis function neural network in phase diffraction-based optical characterization systems to determine the thickness of nanoscale polymer films. The non-stationary measurement data with environmental and detector noise were subjected to a detailed analysis. The outcomes of this investigation are benchmarked against the linear discriminant analysis method and further verified by means of scanning electron microscopy. The results show that the neural network has reached a remarkable accuracy of 98% and 82.5%, respectively, in tests with simulation and experimental data. In this way, rapid and precise thickness estimation may be realized within the tolerance range of 25 nm, offering a significant improvement over conventional measurement techniques.
URI: https://doi.org/10.1088/1361-6501/aced19
https://hdl.handle.net/11147/13765
ISSN: 0957-0233
1361-6501
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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