Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Conference Object Citation - WoS: 1Citation - Scopus: 1Görgül kip ayrıştırması kullanılarak optik faz kırınımında hassasiyet iyileştirilmesi(IEEE, 2023) Ataç, Enes; Dinleyici, Mehmet SalihPhase diffraction is a potent property used in transparent dielectric film characterization. The measured diffraction pattern on the camera is evaluated by matching numerically computed diffraction patterns to determine the optical properties of the ultra-thin films (refractive index, thickness, etc.). However, the obtained diffraction data is not only a nonlinear and non-stationary signal but also exhibits micron-scale variations, thus limiting the measurement accuracy. Therefore, it is challenging to identify shifts in minima and deviations in amplitude on diffraction data to extract information about the optical properties of phase objects. In this study, it is aimed to improve the thickness sensitivity of the system by applying Empirical Mode Decomposition (EMD) to plane wave-based near-field phase diffraction data. Since EMD is very sensitive to abrupt changes in the signal due to the spatial frequency components, the nanoscale variations in the film thickness become more observable and detectable. Experimental outputs and numerical simulations show that the decomposition increases the thickness sensitivity comparing the classical matching technique.Article Enhancing Thickness Determination of Nanoscale Dielectric Films in Phase Diffraction-Based Optical Characterization Systems With Radial Basis Function Neural Networks(IOP Publishing, 2023) Ataç, Enes; Karatay, Anıl; Dinleyici, Mehmet SalihAccurate 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.Article Citation - WoS: 5Citation - Scopus: 5Nanoscale Curved Dielectric Film Characterization Beyond Diffraction Limits Using Spatially Structured Illumination(Academic Press, 2020) Ataç, Enes; Dinleyici, Mehmet SalihOptical fiber based sensor systems often utilize thin dielectric films coated on non-planar surfaces are needed to be inspected for quality assurance. However, non-destructive optical characterization of these films is not a simple method especially on curved large surfaces. In this study, we propose a real time procedure to estimate the optical properties of sub-wavelength transparent dielectric films coated on optical fibers. The paper includes developing a mathematical model and its experimental verification. The near field phase diffraction method is combined with the structured light illumination that is spatial modes of optical fibers to estimate the thickness of the phase object beyond the classical diffraction limits. Numerical simulations and experimental results show that the film thickness can safely be characterized up to one tenth of wavelength of interest via selective spatial field distribution determined according to the morphology of the thin film. The outcomes have good agreements with destructive Scanning Electron Microscope (SEM) measurements. © 2020 Elsevier Inc.