Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning. (25th May 2021)

• Record Type: Journal Article
• Authors: Chen, Yun; Chen, Yanhui; Long, Junyu; Shi, Dachuang; Chen, Xin; Hou, Maoxiang; Gao, Jian; Liu, Huilong; He, Yunbo; Fan, Bi; Wong, Ching-Ping; Zhao, Ni
• Is Part Of: International journal of extreme manufacturing. Volume 3:Number 3(2021)
• Publication Date: 2021-05-25
• Subjects: sub-10 nm silicon nanopore array -- metal-assisted chemical etching -- silica-coated gold nanoparticles -- self-assembly -- machine learning; Manufacturing processes -- Periodicals; Manufacturing processes -- Technological innovations -- Periodicals; 670
• DOI: 10.1088/2631-7990/abff6a
• Languages: English
• ISSNs: 2631-7990

Abstract

Abstract: 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.