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Original Article Open Access
Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures
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Abstract
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%.-
Keywords:
- 3D nanonetwork /
- nanostructures /
- optical properties /
- artificial neural network.
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References
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Figure 1.
Schematic of fabrication set-up. Figure reproduced with permission from ref.6, Elsevier.
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Figure 2.
Filtered reflectance data.
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Figure 3.
Filtered transmittance data.
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Figure 4.
Model validation with experimental transmittance.
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Figure 5.
Refractive index as a function of wavelength.
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Figure 6.
Extinction coefficient as a function of wavelength.
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Figure 7.
PUMA model validation with analytical refractive index (n); R_Puma: Simulation results when only reflectance was input; B_Puma: Simulation results when both reflectance and transmittance were inputs.
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Figure 9.
PUMA model validation with analytical extinction coefficient (k).
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Figure 8.
PUMA model validation with analytical extinction coefficient (k).
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Figure 10.
PUMA model evaluation with experimental transmittance.
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Figure 11.
PUMA model evaluation with experimental reflectance.
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Figure 12.
Flowchart for deep learning algorithm.
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Figure 13.
Deep Learning Model developed.
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Figure 14.
Comparison of model-predicted extinction coefficient with analytical values.
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Figure 15.
Comparison of model-predicted refractive index with analytical values.
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Figure 16.
Absorption regions for fabricated silicon thin film.
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Figure 17.
Tauc’s Plot for determining optical bandgap.
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