Efficient Characterization of Features in Micro-Photoluminescence Images for the Identification of Single-Photon Emitters

Solid-state single-photon emitters (SPE) are a basis for quantum technologies. However, there are many potential SPE that have yet to be explored. The discovery of new SPE typically relies on time-consuming techniques for identifying point source emitters by eye in 2-dimensional (2D) photoluminescence (PL) scans. This manual strategy is a bottleneck for discovering new SPE, suggesting a need for a more efficient method for SPE discovery. Here we present a quantitative method using image analysis and regression fitting to automatically identify Gaussian emitters in 2D PL scans and classify them according to their intensity and stability. We demonstrate efficient emitter classification for nanodiamond arrays and hexagonal boron nitride (hBN) flakes. Flexible criteria detect SPE in both samples despite variation in emitter intensity, stability, and background quality. The detection criteria can be tuned to unique material systems and experimental setups to accommodate the diverse properties of SPE.