Neural network approach to rapid thin film characterization

Jakatdar, Nickhil H.; Niu, Xinhui; Spanos, Costas J.

doi:10.1117/12.304402

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…demonstrated and trained a very basic single layer Back Propagation system to evaluate the optical constants of a mono-layer thin film. Tabet et al 43 . have implemented ANNs to predict the fitting constants in Cauchy's dispersion model, which were integrated in a measurement program to find film thickness and refractive indices of transparent films in the visible region.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures

Joshi¹,

Micro²,

Kiani³

2021

OEA

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The aim of this study is to develop a reliable method to determine optical constants for 3D-nanonetwork Si thin films manufactured using a pulsed-laser ablation technique that can be applied to other materials synthesized by this technique. An analytical method was introduced to calculate optical constants from reflectance and transmittance spectra. Optical band gaps for this novel material and other important insights on the physical properties were derived from the optical constants. The existing optimization methods described in the literature were found to be complex and prone to errors while determining optical constants of opaque materials where only reflectance data is available. A supervised Deep Learning Algorithm was developed to accurately predict optical constants from the reflectance spectrum alone. The hybrid method introduced in this study was proved to be effective with an accuracy of 95%.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures

Joshi¹,

Micro²,

Kiani³

2021

OEA

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“…The network designated as NN4 has three neurons in the output layer that simultaneously predict all three mentioned parameters. The bases formed for the training of the first three networks were made individually (Base 1, Base 2 and Base 3), while the training base NN4 (Base 4) was made by merging all three individual bases [ 67 , 68 , 69 , 70 ].…”

Section: Network Structurementioning

confidence: 99%

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

et al. 2023

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In this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO2 film deposited on a silicon substrate, with a thickness of 30 μm, in the frequency range of 20 to 20 kHz with neural networks were analysed. For this purpose, the geometric (thickness), thermal (thermal diffusivity, coefficient of linear expansion) and electronic parameters of substrates were known and constant in the two-layer model, while the following nano-layer thin-film parameters were changed: thickness, expansion and thermal diffusivity. Predictions of these three parameters of the thin-film were analysed separately with three neural networks. All of them together were joined by a fourth neural network. It was shown that the neural network, which analysed all three parameters at the same time, achieved the highest accuracy, so the use of networks that provide predictions for only one parameter is less reliable. The obtained results showed that the application of neural networks in determining the thermoelastic properties of a thin film on a supporting substrate enables the estimation of its characteristics with great accuracy.

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“…In these applications, high yields, high equipment utilization and high throughput are economically important, making real time in situ monitoring essential to their manufacturing processes [2]. As such, the manufacturing engineers need prior knowledge of some essential film properties such as thickness, deposition rate, resistivity and standard deviation of sheet resistance to monitor the performance status of the deposition tool and provide decision supports.…”

Section: Introductionmentioning

confidence: 99%

Functional networks models for rapid characterization of thin films: An application to ultrathin polycrystalline silicon germanium films

Asafa

Adeniran

Olatunji

2015

Applied Soft Computing

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Neural network approach to rapid thin film characterization

Cited by 7 publications

References 5 publications

Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures

Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

Functional networks models for rapid characterization of thin films: An application to ultrathin polycrystalline silicon germanium films

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