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arXiv · 2510.06076

Universal computational super-resolution framework for imaging of quantum dots

Dominik Vašinka·Jaewon Lee·Charlie Stalker·Victor Mitryakhin·Ivan Solovev·Sven Stephan·Sven Höfling·Falk Eilenberger·Seth Ariel Tongay·Christian Schneider·Miroslav Ježek·Ana Predojević
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Abstract

We present a universal deep-learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement. Trained on physics-based synthetic data spanning diverse point-spread functions, aberrations, and noise, the network generalizes across experimental conditions without system-specific retraining. We validate the approach on low- and high-density In(Ga)As quantum dots and strain-induced dots in 2D monolayer WSe$_2$, resolving overlapping emitters even under low signal-to-noise and inhomogeneous backgrounds. By eliminating calibration and iterative acquisitions, this single-shot strategy enables rapid, robust computational super-resolution for nanoscale characterization and quantum photonic device fabrication.

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Dominik Vašinka, Jaewon Lee, Charlie Stalker, Victor Mitryakhin, Ivan Solovev, Sven Stephan, Sven Höfling, Falk Eilenberger, Seth Ariel Tongay, Christian Schneider, Miroslav Ježek, Ana Predojević. 2025-10-07. Universal computational super-resolution framework for imaging of quantum dots. https://doi.org/10.1021/acsnanoscienceau.5c00198

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