2025/10/07 by Dominik Vašinka, Jae‐Won Lee, Vašinka, Dominik +20
Biochemistry, Genetics and Molecular Biology · Engineering · #Advanced Fluorescence Microscopy Techniques #FOS: Physical sciences #Integrated Circuits and Semiconductor Failure Analysis #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Near-Field Optical Microscopy #Optics (physics.optics) #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2510.06076
openalex publication_date 2025/10/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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 WSe2, 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 super-resolution for nanoscale characterization and quantum photonic device fabrication.