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In a new publication by Opto-Electronic Advances; DOI 10.29026/oea.2021.210039, Shreeniket Joshi and Amirkianoosh Kiani of the University of Ontario Institute of Technology, Ontario, Canada, discuss a hybrid artificial method for predicting the optical constants and band gap energy of 3D nano-network silicon structures Neural networks and analytical models.
This study introduces a reliable method to determine the optical properties of new silicon thin films (nanomaterials). By bombarding the silicon wafer with a pulsed laser beam, a silicon thin film is deposited on the glass. Due to the limited experimental data available, finding the optical properties of new nanomaterials is challenging. Existing models used to find optical properties are found to be complex and error-prone. This study proposes a new method that combines analysis models with artificial neural networks. The purpose of using artificial neural networks is to develop a mathematical function to predict the optical constants of new films. It is found that the accuracy of the proposed method is 95%.
The research team of Dr. Amirkianoosh Kiani of the University of Ontario Institute of Technology proposed this study to find the optical properties of new silicon thin films, and the method was verified by accurate and reliable evidence. For transparent new materials, experimental data of transmittance and reflectance can be used to determine optical properties. However, it is challenging to do the same for opaque materials, because in this case only reflectance data is available. This research can be used to establish mathematical relationships between available experimental data and show the potential to predict the optical properties of opaque materials only from reflectance data.
In addition, it is found that the optical properties of the new silicon thin film discussed in this study have an energy band gap of 1.648, which is close to the material used to collect solar energy. Because silicon thin films have an amazing surface area, materials with this energy band gap can prove to be very efficient in solar applications. The research team also intends to use this method to stimulate materials such as titanium dioxide and gold nanoparticles used in biomedical applications.
Article reference: Joshi S, Kiani A. Hybrid artificial neural network and analysis model used to predict the optical constants and band gap energy of 3D nano-network silicon structures. Optoelectronics Adv 4, 210039 (2021). doi: 10.29026/oea.2021.210039
Keywords: 3D nano network/nano structure/optical properties/artificial neural network
Silicon Hall is a research laboratory at the University of Ontario Institute of Technology in Oshawa, Ontario, Canada (July 2017 to present). The laboratory was established in 2014 at the University of New Brunswick, Canada, and has been led by Dr. Amirkianoosh Kiani since then. Silicon Hall has been commissioned as a center to study the interaction of laser materials, especially those involving micro/nano manufacturing, laser material processing, energy storage systems, micro and nano materials, and bio-nano technology.
Opto-Electronic Advances (OEA) is a high-impact, open-access, peer-reviewed monthly SCI journal with an impact factor of 9.682 (journal citation report for IF 2020). Since its launch in March 2018, OEA has been included in SCI, EI, DOAJ, Scopus, CA and ICI databases, and its editorial board has expanded to 33 members from 17 countries and regions (average h-index 46).
Published by the Institute of Optoelectronics, Chinese Academy of Sciences, the magazine aims to provide a platform for researchers, academicians, professionals, practitioners and students to impart and share knowledge in the form of high-quality empirical and theoretical research papers, covering optics and photonics. The subject of science and optoelectronics.
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