Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning

• 作者: 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
• 来源期刊: 极端制造(英文)
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: APPROACHES AND THEORIES OF PROCESSING
• 页码范围: 83-93
• 页数: 11页
• 语种: 中文

摘要

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.