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

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.