Efficient Analysis of Photoluminescence Images for the Classification of Single-Photon Emitters

Abstract

Solid-state single-photon emitters (SPE) are a basis for emerging technologies such as quantum communication and quantum sensing. SPE based on fluorescent point defects are ubiquitous in semiconductors and insulators, and new systems with desirable properties for quantum information science may exist among the vast number of unexplored defects. However, the characterization of new SPE typically relies on time-consuming techniques for identifying point sources by eye in photoluminescence (PL) images. This manual strategy is a bottleneck for discovering new SPE, motivating a more efficient method for characterizing emitters in PL images. Here, we present a quantitative method using image analysis and regression fitting to automatically identify Gaussian emitters in PL images and classify them according to their stability, shape, and intensity relative to the background. We demonstrate efficient emitter classification for SPE in nanodiamond arrays and hexagonal boron nitride flakes. Adaptive criteria detect SPE in both samples despite variation in emitter intensity, stability, and background features. The detection criteria can be tuned for specific material systems and experimental setups to accommodate the diverse properties of SPE.

Single-Photon Emission from Two-Dimensional Materials, to a Brighter Future

“…Fast photoluminescence mapping of the emission intensity and line width is the first step in identifying SPEs. Machine learning algorithms which can quickly analyze the mapping data and identify highly possible SPE “hot spots” is desirable . Additionally, measuring the photon correlation for rapid classification of SPEs in autocorrelation measurement is being realized with machine learning .…”

“…Machine learning algorithms which can quickly analyze the mapping data and identify highly possible SPE "hot spots" is desirable. 85 Additionally, measuring the photon correlation for rapid classification of SPEs in autocorrelation measurement is being realized with machine learning. 86 Practical applications require the generated single photons to be efficiently read and manipulated; however, these have not been achieved yet in 2D materials.…”

Heterogeneous Integration of Solid-State Quantum Systems with a Foundry Photonics Platform

“…17,19,20 However, challenges remain to achieve integration using foundry-manufacturable photonics in a scalable process. In particular, the need to identify 21,22 and manipulate stochastic emitters 23,24 requires measurement over sample areas (≈mm 2 ) with nanoscale precision and is a highly sensitive and time-intensive technique.…”

“…Miniaturization of photonics and electronics has enabled selective control and manipulation on the same chip. , Multiple attempts have been shown to integrate solid-state emitters with in-house-fabricated silicon photonics. − A pick-and-place method has been adopted to combine diamond-hosted color centers with integrated photonics. ,, However, challenges remain to achieve integration using foundry-manufacturable photonics in a scalable process. In particular, the need to identify , and manipulate stochastic emitters , requires measurement over sample areas (≈mm 2 ) with nanoscale precision and is a highly sensitive and time-intensive technique.…”