Probe conditioning via convolution neural network for scanning probe microscopy automation

被引：8

作者：

Diao, Zhuo ^{[1
]}

Hou, Linfeng ^{[1
]}

Abe, Masayuki ^{[1
]}

机构：

[1] Osaka Univ, Grad Sch Engn Sci, 1-3 Machikaneyama, Toyonaka, Osaka 5600043, Japan

来源：

APPLIED PHYSICS EXPRESS | 2023年 / 16卷 / 08期

关键词：

deep learning; scanning probe microscopy; scanning tunneling microscopy; image recognition; SPM tip; autonomous experiments; convolutional neural network; ATOM MANIPULATION;

D O I：

10.35848/1882-0786/acecd6

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

We present an automation system for conditioning a scanning probe microscopy (SPM) probe into different states on a Si(111)-(7 x 7) surface at room temperature. Topography images representing multiple surface states and probe condition states divided into 11 categories and trained by a convolution neural network with an accuracy of 87% were used to estimate the effectiveness of the probe with an accuracy of 98%. We demonstrate the responsiveness of the method by experimentally reforming a probe into different conditions defined by preset categories. This system will promote advancements in autonomous SPM experiments at atomic scale and room temperature.

引用

页数：5

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