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

In a new publication from Opto-Electronic Advances; DOI 10.29026/oea.2021.210039, Shreeniket Joshi and Amirkianoosh Kiani from Ontario Tech University, Ontario, Canada discuss hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures.

This study introduces a reliable method to determine optical properties for novel silicon thin films (nanomaterial). Silicon thin films were deposited on glass by bombarding silicon wafers with pulsed laser beams. Finding optical properties of novel nanomaterials is challenging as limited experimental data is available. The existing models for finding optical properties were found to be complex and prone to errors, this study proposes a new method of using analytical models with artificial neural networks. The purpose of using artificial neural networks was to develop a mathematical function to predict optical constants for novel thin films. This method proposed was found to be 95% accurate.

The research group of Dr. Amirkianoosh Kiani from Ontario Tech University proposed this study to find optical properties of novel silicon thin films and the method was validated with conclusive proof to be accurate and reliable. For transparent novel materials, optical properties can be determined using experimental data for transmittance and reflectance. However, it’s challenging to do the same for opaque materials as in this case only reflectance data is available. This study can be used to establish a mathematical relation between the available experimental data and shows promising potential for predicting optical properties for opaque materials from reflectance data alone.

Further, the optical properties determined for the novel silicon thin film discussed in this study was found to have an energy band gap of 1.648, this value is close to materials used for harvesting solar energy. As silicon thin films have a phenomenal surface area, a material with this energy band gap can prove to be highly efficient in solar applications. The research group also intends to use this method for exciting materials like titania, gold nanoparticles, etc. which are used in biomedical applications.

 

Article reference: Joshi S, Kiani A. Hybrid artificial neural networks and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork silicon structures. Opto-Electron Adv 4, 210039 (2021) . doi: 10.29026/oea.2021.210039 

Keywords: 3D Nanonetwork / Nanostructures / Optical Properties / Artificial Neural Network

# # # # # #

 

Silicon Hall is a scientific research laboratory which is part of the Ontario Tech University, located in Oshawa, Ontario, Canada (July 2017 – Present). The laboratory was established in 2014 at University of New Brunswick, NB, Canada and its operations since then have been led by Dr Amirkianoosh Kiani. Silicon Hall has been commissioned to serve as a centre for investigations of laser material interaction, specifically those involving micro/nanofabrication, laser materials processing, micro and nanomaterials for energy storage systems, and bionanotechnology.

# # # # # #

 

Opto-Electronic Advances (OEA) is a high-impact, open access, peer reviewed monthly SCI journal with an impact factor of 9.682 (Journals Citation Reports for IF 2020). Since its launch in March 2018, OEA has been indexed in SCI, EI, DOAJ, Scopus, CA and ICI databases over the time and expanded its Editorial Board to 33 members from 17 countries and regions (average h-index 46).

The journal is published by The Institute of Optics and Electronics, Chinese Academy of Sciences, aiming at providing 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 the topics of optics, photonics and optoelectronics.

 

# # # # # #

 

More information: http://www.oejournal.org/oea

Editorial Board: http://www.oejournal.org/oea/editorialboard/list

All issues available in the online archive (http://www.oejournal.org/oea/archive).

Submissions to OEA may be made using ScholarOne (https://mc03.manuscriptcentral.com/oea).

ISSN: 2096-4579

CN: 51-1781/TN

Contact Us: oea@ioe.ac.cn

Twitter: @OptoElectronAdv (https://twitter.com/OptoElectronAdv?lang=en)

WeChat: OE_Journal

 

# # # # # #

---

---