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Energy-efficient neural network inference with microcavity exciton-polaritons

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 Added by Michal Matuszewski
 Publication date 2021
and research's language is English




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We propose all-optical neural networks characterized by very high energy efficiency and performance density of inference. We argue that the use of microcavity exciton-polaritons allows to take advantage of the properties of both photons and electrons in a seamless manner. This results in strong optical nonlinearity without the use of optoelectronic conversion. We propose a design of a realistic neural network and estimate energy cost to be at the level of attojoules per bit, also when including the optoelectronic conversion at the input and output of the network, several orders of magnitude below state-of-the-art hardware implementations. We propose two kinds of nonlinear binarized nodes based either on optical phase shifts and interferometry or on polariton spin rotations.



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Due to high binding energy and oscillator strength, excitons in thin flakes of transition metal dichalcogenides constitute a perfect foundation for realizing a strongly coupled light-matter system. In this paper we investigate mono- and few-layer WSe$_2$ flakes encapsulated in hexagonal boron nitride and incorporated into a planar dielectric cavity. We use an open cavity design which provides tunability of the cavity mode energy by as much as 150 meV. We observe a strong coupling regime between the cavity photons and the neutral excitons in direct-bandgap monolayer WSe$_2$, as well as in few-layer WSe$_2$ flakes exhibiting indirect bandgap. We discuss the dependence of the excitons oscillator strength and resonance linewidth on the number of layers and predict the exciton-photon coupling strength.
179 - B. Pietka , D. Zygmunt , M. Krol 2014
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