Projects per year
Abstract
Scanning probe microscopy (SPM) has revolutionized the fields of materials, nano-science, chemistry, and biology, by enabling mapping of surface properties and surface manipulation with atomic precision. However, these achievements require constant human supervision; fully automated SPM has not been accomplished yet. Here we demonstrate an artificial intelligence framework based on machine learning for autonomous SPM operation (DeepSPM). DeepSPM includes an algorithmic search of good sample regions, a convolutional neural network to assess the quality of acquired images, and a deep reinforcement learning agent to reliably condition the state of the probe. DeepSPM is able to acquire and classify data continuously in multi-day scanning tunneling microscopy experiments, managing the probe quality in response to varying experimental conditions. Our approach paves the way for advanced methods hardly feasible by human operation (e.g., large dataset acquisition and SPM-based nanolithography). DeepSPM can be generalized to most SPM techniques, with the source code publicly available.
Original language | English |
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Article number | 54 |
Number of pages | 8 |
Journal | Communications Physics |
Volume | 3 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Dec 2020 |
Projects
- 1 Active
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ARC Centre of Excellence in Future Low-energy Electronics Technologies
Fuhrer, M., Bao, Q., Culcer, D., Davis, M., Davis, J. A., Hamilton, A., Helmerson, K., Kalantar-Zadeh, K., Klochan, O., Medhekar, N., Ostrovskaya, E., Parish, M., Schiffrin, A., Seidel, J., Sushkov, O., Valanoor, N., Vale, C., Wang, X., Wang, L., Galitskiy, V., Gurarie, V., Hannon, J., Höfling, S., Hone, J., Rule, K. C., Krausz, F., Littlewood, P., MacDonald, A., Neto, A., Oezyilmaz, B., Paglione, J., Phillips, W., Refael, G., Spielman, I., Tadich, A., Xue, Q., Cole, J., Perali, A., Neilson, D., Lin, H., Sek, G., Gaston, N., Hodgkiss, J. M., Tang, M., Karel, J., Nguyen, T., Adam, S. & Granville, S.
Australian Research Council (ARC), Monash University – Internal School Contribution, Monash University – Internal Department Contribution, Monash University – Internal Faculty Contribution, Monash University – Internal University Contribution, University of Wollongong, University of Queensland , Tsinghua University, University of New South Wales, Australian National University , RMIT University, Swinburne University of Technology
29/06/17 → 28/06/24
Project: Research