Super-resolution imaging of quantum emitters in layered materials
Mehran Kianinia, Carlo Bradac, Fan Wang, Bernd Sontheimer, Toan Trong, Tran, Minh Nguyen, Sejeong Kim, Zai-Quan Xu, Dayong Jin, Andreas W. Schell,, Charlene J. Lobo, Igor Aharonovich, Milos Toth

TL;DR
This paper demonstrates super-resolution imaging of quantum emitters in layered hBN using ground state depletion nanoscopy, revealing nonlinear photophysical properties and enabling high-precision localization for advanced nanophotonics and bio-imaging.
Contribution
It introduces a super-resolution imaging technique for quantum emitters in layered hBN with 63 nm resolution and explores nonlinear photophysical properties for improved imaging.
Findings
Achieved 63 nm spatial resolution in imaging SPEs in hBN.
Discovered nonlinear photophysical properties of SPEs in hBN.
Developed a low-power GSD variant using dual doughnut-shaped lasers.
Abstract
Layered van der Waals materials are emerging as compelling two-dimensional (2D) platforms for studies of nanophotonics, polaritonics, valleytronics and spintronics, and have the potential to transform applications in sensing, imaging and quantum information processing. Amongst these, hexagonal boron nitride (hBN) is unique in that it hosts ultra-bright, room temperature single photon emitters (SPEs). However, an outstanding challenge is to locate SPEs in hBN with high precision, a task which requires breaking the optical diffraction limit. Here, we report the imaging of SPEs in layered hBN with a spatial resolution of 63 nm using ground state depletion (GSD) nanoscopy. Furthermore, we show that SPEs in hBN possess nonlinear photophysical properties which can be used to realize a new variant of GSD that employs a coincident pair of doughnut-shaped lasers to reduce the laser power that is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
