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 ...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.展开更多
基金supported by the National Natural Science Foundation of China[Grant Nos.51975127,U20A6004]the Guangdong-Hong Kong Technology Coopeartion[Grant No.GHP/112/19GD]from Hong Kong Innovation and Technology Commission+1 种基金Research and Development Program of Guangdong Province[Grant No.2020A0505140008]the Fund of Key-Area Research and Development Program of Guangdong Province[Grant No.2018B090906002]。
文摘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.
