Universal Computational Super-Resolution Framework for Imaging of Quantum Dots
Abstract
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 WSe2, resolving overlapping emitters even under low signal-to-noise ratios 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.
Fields of Study & Subjects
Near-Field Optical Microscopy
Advanced Fluorescence Microscopy Techniques
Integrated Circuits and Semiconductor Failure Analysis
Publication details
- Journal / source
- ACS Nanoscience Au
- Publisher
- American Chemical Society (ACS)
- Volume
- 6
- Issue
- 5
- Pages
- 842-849
- Publication date
- 5 August 2026
- Language
- EN