Uncooled Poisson Bolometer for High-Speed Event-Based Long-wave Thermal Imaging

Event-based vision provides high-speed, energy-efficient sensing for applications such as autonomous navigation and motion tracking. However, implementing this technology in the long-wave infrared remains a significant challenge. Traditional infrared sensors are hindered by slow thermal response times or the heavy power requirements of cryogenic cooling. Here, we introduce the first event-based infrared detector operating in a Poisson-counting regime. This is realized with a spintronic Poisson bolometer capable of broadband detection from 0.8-14$μ\text{m}$. In this regime, infrared signals are detected through statistically resolvable changes in stochastic switching events. This approach enables room-temperature operation with high timing resolution. Our device achieves a maximum event rate of 1,250 Hz, surpassing the temporal resolution of conventional uncooled microbolometers by a factor of 4. Power consumption is kept low at 0.2$μ$W per pixel. This work establishes an operating principle for infrared sensing and demonstrates a pathway toward high-speed, energy-efficient, event-driven thermal imaging.

Physics-Integrated Inference for Signal Recovery in Non-Gaussian Regimes

High-performance room-temperature sensing is often limited by non-stationary $1/f$ fluctuations and non-Gaussian stochasticity. In spintronic devices, thermally activated Néel switching creates heavy-tailed noise that masks weak signals, defeating linear filters optimized for Gaussian statistics. Here, we introduce a physics-integrated inference framework that decouples signal morphology from stochastic transients using a hierarchical 1D CNN-GRU topology. By learning the temporal signatures of Néel relaxation, this architecture reduces the Noise Equivalent Differential Temperature (NEDT) of spintronic Poisson bolometers by a factor of six (233.78 mK to 40.44 mK), effectively elevating room-temperature sensitivity toward cryogenic limits. We demonstrate the framework's universality across the electromagnetic and biological spectrum, achieving a 9-fold error suppression in Radar tracking, a 40\% uncertainty reduction in LiDAR, and a 15.56 dB SNR enhancement in ECG. This hardware-inference coupling recovers deterministic signals from fluctuation-dominated regimes, enabling near-ideal detection limits in noisy edge environments.

Overcoming the Rayleigh limit in extremely low SNR

Overcoming the diffraction limit and addressing low Signal-to-Noise Ratio (SNR) scenarios have posed significant challenges to optical imaging systems in applications such as medical diagnosis, remote sensing, and astronomical observations. In this study, we introduce a novel Stochastic Sub-Rayleigh Imaging (SSRI) algorithm capable of localizing point sources and estimating their positions, brightness, and number in low SNR conditions and within the diffraction limit. The SSRI algorithm utilizes conventional imaging devices, facilitating practical and adaptable solutions for real-world applications. Through extensive experimentation, we demonstrate that our proposed method outperforms established algorithms, such as Richardson-Lucy deconvolution and CLEAN, in various challenging scenarios, including extremely low SNR conditions and large relative brightness ratios. We achieved between 40% and 80% success rate in estimating the number of point sources in experimental images with SNR less than 1.2 and sub-Rayleigh separations, with mean position errors less than 2.5 pixels. In the same conditions, the Richardson-Lucy and CLEAN algorithms correctly estimated the number of sources between 0% and 10% of the time, with mean position errors greater than 5 pixels. Notably, SSRI consistently performs well even in the sub-Rayleigh region, offering a benchmark for assessing future quantum superresolution techniques. In conclusion, the SSRI algorithm presents a significant advance in overcoming diffraction limitations in optical imaging systems, particularly under low SNR conditions, with potential widespread impact across multiple fields like biomedical microscopy and astronomical imaging.