  During the past decade, interest has grown significantly in developing
ultrasensitive widefield diamond magnetometry for various applications. Despite
attempts to improve the adoption of conventional frame-based sensors, achieving
high temporal resolution and sensitivity simultaneously remains a key
challenge. This is largely due to the transfer and processing of massive
amounts of sensor data to capture the widefield fluorescence intensity changes
of spin defects in diamonds. In this study, we adopt a neuromorphic vision
sensor to address this issue. This sensor pre-processes the detected signals in
optically detected magnetic resonance (ODMR) measurements for quantum sensing,
employing a working principle that closely resembles the operation of the human
vision system. By encoding the changes of light intensity into spikes, this
approach results in a vast dynamic range, high temporal resolution, and
exceptional signal-to-background ratio. After a thorough evaluation of
theoretical feasibility, our experiment with an off-the-shelf event camera
demonstrated a 13x improvement in temporal resolution with comparable precision
of detecting ODMR resonance frequencies compared with the state-of-the-art
highly specialized frame-based approach. A specialized camera system with the
same mechanism has the potential to enhance these benefits further. This
performance improvement is primarily attributable to orders of magnitude
smaller data volumes and, thus, reduced latency. We further showcase the
deployment of this technology in monitoring dynamically modulated laser heating
of gold nanoparticles coated on a diamond surface, a recognizably difficult
task using existing approaches. The current development provides new insights
for high-precision and low-latency widefield quantum sensing, with
possibilities for integration with emerging memory devices for more efficient
event-based data processing.
