Author Affiliations
Abstract
1 State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, School of Electromechnical Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
2 School of Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
3 Guangdong ADA Intelligent Equipment Ltd, Foshan 510006, People’s Republic of China
4 Institute of Business Analysis and Supply Chain Management, College of Management, Shenzhen University, Shenzhen, People’s Republic of China
5 School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
Solid-state nanopores with controllable pore size and morphology have huge application potential. However, it has been very challenging to process sub-10 nm silicon nanopore arrays with high efficiency and high quality at low cost. In this study, a method combining metal-assisted chemical etching and machine learning is proposed to fabricate sub-10 nm nanopore arrays on silicon wafers with various dopant types and concentrations. Through a SVM algorithm, the relationship between the nanopore structures and the fabrication conditions, including the etching solution, etching time, dopant type, and concentration, was modeled and experimentally verified. Based on this, a processing parameter window for generating regular nanopore arrays on silicon wafers with variable doping types and concentrations was obtained. The proposed machine-learning-assisted etching method will provide a feasible and economical way to process high-quality silicon nanopores, nanostructures, and devices. Supplementary material for this article is available online
sub-10 nm silicon nanopore array metal-assisted chemical etching silica-coated gold nanoparticles self-assembly machine learning International Journal of Extreme Manufacturing
2021, 3(3): 035104
