Optically driven nano-motors designed by machine learning

Mintae Chung; Karim Achouri; Olivier J. F. Martin

doi:10.1117/12.2633430

3 October 2022 Optically driven nano-motors designed by machine learning

Mintae Chung, Karim Achouri, Olivier J. F. Martin

Proceedings Volume PC12198, Optical Trapping and Optical Micromanipulation XIX; PC121980M (2022) https://doi.org/10.1117/12.2633430
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Nano-motors driven by linearly polarized light were fabricated and measured experimentally. These structures include a plasmonic rotor embedded into a SiO2 body. The rotor geometry was optimized to reach the strongest torque using a convolutional neural network connected to a deep convolution generative adversarial network. The most promising nanostructures were fabricated with a multistep process that included ion beam etching of the rotor, followed by embodiment in SiO2. Careful optimization enabled the realization of sub-20 nm features. The nano-motors were transferred to a fluidic chamber for optical characterization, demonstrating rapid rotation speeds.

Conference Presentation

Citation Download Citation

Mintae Chung, Karim Achouri, and Olivier J. F. Martin "Optically driven nano-motors designed by machine learning", Proc. SPIE PC12198, Optical Trapping and Optical Micromanipulation XIX, PC121980M (3 October 2022); https://doi.org/10.1117/12.2633430

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