(Peer-Reviewed) Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures
Shreeniket Joshi, Amirkianoosh Kiani
Silicon Hall: Micro/Nano Manufacturing Facility, Faculty of Engineering and Applied Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, Ontario L1G 0C5, Canada
Opto-Electronic Advances, 2021-09-25
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%.
A hybrid integrated high-precision tunable semiconductor laser
Yiran Zhu, Botao Fu, Zhiwei Fang, Qiyue Hu, Jianping Yu, Yunpeng Song, Yu Ma, Min Wang, Kunpeng Jia, Zhenda Xie, Ya Cheng
Opto-Electronic Advances
2026-02-12
Millisecond-level electrically switchable metalens for adaptive rotational depth mapping and diffraction-limited imaging
Yeseul Kim, Jihae Lee, Won-Sik Kim, Hyeonsu Heo, Dongmin Jeon, Beomha Yang, Xiaotong Li, Harit Keawmuang, Shiqi Hu, Young-Ki Kim, Trevon Badloe, Junsuk Rho
Opto-Electronic Advances
2026-02-12
