Optofluidic memory and self-induced nonlinear optical phase change for reservoir computing in silicon photonics

Chengkuan Gao, Prabhav Gaur, Dhaifallah Almutairi, Shimon Rubin () and Yeshaiahu Fainman
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Chengkuan Gao: University of California
Prabhav Gaur: University of California
Dhaifallah Almutairi: University of California
Shimon Rubin: University of California
Yeshaiahu Fainman: University of California

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Nanophotonics allows to employ light-matter interaction to induce nonlinear optical effects and realize non-conventional memory and computation capabilities, however to date, light-liquid interaction was not considered as a potential mechanism to achieve computation on a nanoscale. Here, we experimentally demonstrate self-induced phase change effect which relies on the coupling between geometric changes of thin liquid film to optical properties of photonic waveguide modes, and then employ it for neuromorphic computing. In our optofluidic silicon photonics system we utilize thermocapillary-based deformation of thin liquid film capable to induce nonlinear effect which is more than one order of magnitude higher compared to the more traditional heat-based thermo-optical effect, and allowing operation as a nonlinear actuator and memory element, both residing at the same compact spatial region. The resulting dynamics allows to implement Reservoir Computing at spatial region which is approximately five orders of magnitude smaller compared to state-of-the-art experimental liquid-based systems.

Date: 2023
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DOI: 10.1038/s41467-023-40127-x

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