High-precision and low-latency widefield diamond quantum sensing with neuromorphic vision sensors
Zhiyuan Du, Madhav Gupta, Feng Xu +9 more
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.
arXiv:2306.14099•Jun 25, 2023•0 citations