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.

具有可控孔径和形貌的固态纳米孔具有巨大的应用潜力。然而，以低成本高效率、高质量地加工亚 10 nm 硅纳米孔阵列一直是非常具有挑战性的。在这项研究中，提出了一种结合金属辅助化学蚀刻和机器学习的方法，在具有各种掺杂剂类型和浓度的硅片上制造亚 10 nm 纳米孔阵列。通过支持向量机算法，纳米孔结构与制造条件之间的关系，包括蚀刻溶液、蚀刻时间、掺杂剂类型和浓度，被建模和实验验证。在此基础上，获得了用于在具有可变掺杂类型和浓度的硅晶片上生成规则纳米孔阵列的工艺参数窗口。