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Femtosecond switching of strong light-matter interactions in microcavities with two-dimensional semiconductors

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abstract

Ultrafast all-optical logic devices based on nonlinear light-matter interactions hold the promise to overcome the speed limitations of conventional electronic devices. Strong coupling of excitons and photons inside an optical resonator enhances such interactions and generates new polariton states which give access to unique nonlinear phenomena, such as Bose-Einstein condensation, used for all-optical ultrafast polariton transistors. However, the pulse energies required to pump such devices range from tens to hundreds of pJ, making them not competitive with electronic transistors. Here we introduce a new paradigm for all-optical switching based on the ultrafast transition from the strong to the weak coupling regime in microcavities embedding atomically thin transition metal dichalcogenides. Employing single and double stacks of hBN-encapsulated MoS$_2$ homobilayers with high optical nonlinearities and fast exciton relaxation times, we observe a collapse of the 55-meV polariton gap and its revival in less than one picosecond, lowering the threshold for optical switching below 4 pJ per pulse, while retaining ultrahigh switching frequencies. As an additional degree of freedom, the switching can be triggered pumping either the intra- or the interlayer excitons of the bilayers at different wavelengths, speeding up the polariton dynamics, owing to unique interspecies excitonic interactions. Our approach will enable the development of compact ultrafast all-optical logical circuits and neural networks, showcasing a new platform for polaritonic information processing based on manipulating the light-matter coupling.

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Polaritonic Machine Learning for Graph-based Data Analysis

cond-mat.dis-nn · 2025-07-14 · conditional · novelty 6.0

Simulated polariton condensate lattices act as physics-based feature generators for CNNs and improve classification of cliques and asymmetries in point clouds over raw point images in three synthetic tasks.

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  • Polaritonic Machine Learning for Graph-based Data Analysis cond-mat.dis-nn · 2025-07-14 · conditional · none · ref 41 · internal anchor

    Simulated polariton condensate lattices act as physics-based feature generators for CNNs and improve classification of cliques and asymmetries in point clouds over raw point images in three synthetic tasks.