@article{Chen2021, 
author = {Yun Chen and Yanhui Chen and Junyu Long and Dachuang Shi and Xin Chen and Maoxiang Hou and Jian Gao and Huilong Liu and Yunbo He and Bi Fan and Ching-Ping Wong and Ni Zhao},
title = {Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning},
year = {2021},
journal = {International Journal of Extreme Manufacturing},
volume = {3},
number = {3},
pages = {035104},
keywords = {machine learning, self-assembly, metal-assisted chemical etching, sub-10 nm silicon nanopore array, silica-coated gold nanoparticles},
url = {https://www.sciopen.com/article/10.1088/2631-7990/abff6a},
doi = {10.1088/2631-7990/abff6a},
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.}
}