Affiliation:
1. Department of Physics, Chungbuk National University , Seowon-Gu, Cheongju 28644, South Korea
Abstract
The use of scientific instruments generally requires prior knowledge and skill on the part of operators, and thus, the obtained results often vary with different operators. The autonomous operation of instruments producing reproducible and reliable results with little or no operator-to-operator variation could be of considerable benefit. Here, we demonstrate the autonomous operation of an atomic force microscope using a machine learning-based object detection technique. The developed atomic force microscope was able to autonomously perform instrument initialization, surface imaging, and image analysis. Two cameras were employed, and a machine-learning algorithm of region-based convolutional neural networks was implemented, to detect and recognize objects of interest and to perform self-calibration, alignment, and operation of each part of the instrument, as well as the analysis of obtained images. Our machine learning-based approach could be generalized to apply to various types of scanning probe microscopes and other scientific instruments.
Funder
National Research Foundation of Korea
Ministry of Science and ICT, South Korea
Ministry of Trade, Industry and Energy
The Overseas Dispatch Program of Chungbuk National University in 2022.
Cited by
1 articles.
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