Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning
Yun Chen, Yanhui Chen, Junyu Long, Dachuang Shi, Xin Chen, Maoxiang Hou, Jian Gao, Huilong Liu, Yunbo He, Bi Fan, Ching‐Ping Wong, Ni Zhao
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.