CCSS: Signal Processing for Single-Photon Detectors

光有一个根本上最小的量--一个光子--这与人类的日常经验相去甚远。例如，手机中的摄像头收集的光子数量以万亿计，形成一张典型的照片。然而，有一些越来越常见的设备依赖于测量最小可能的光量。这些都是执行“单光子探测”(SPD)的设备。SPD与脉冲激光结合用于自动驾驶汽车的3D成像系统，并用于在iPad Pro等设备中实现增强现实。许多类型的科学成像也使用SPD来查看单个分子，跟踪蛋白质，或通过光谱分析来确定化学浓度。用于SPD的设备具有复杂的行为，通常被幼稚地建模。这个项目将使用更详细的建模，开发与SPD一起使用的数据处理方法，以改进各种系统。尽管SPD即将进入日常使用，但SPD的信号处理已经远远落后。系统设计和设备设计都遵循权衡原则，而这些权衡在很大程度上取决于数据处理的复杂程度。因此，新的信号处理不仅将改进使用SPD的应用，还将影响硬件设计。具有单光子灵敏度的设备受到这样的限制：每个探测事件都会导致非零的“死时间”，在此期间系统无法记录入射粒子。由于缓解死区时间效应的信号处理方法很少，也不为人所知，因此通常要小心避免操作系统的死区时间效应，从而使光子很少在死区时间到达。虽然这确实使死时间的影响可以忽略不计，但人们自然会问，这是不是一个好做法。初步结果表明，允许明显的死区效应并对其进行补偿可以显著提高激光雷达的性能。一个项目的重点是改进和利用死区效应的建模，为激光雷达和其他应用程序创建最具信息量的测量。在用于SPD的阵列中，保持低串扰是增加填充因子从而提高检测效率的主要障碍。另一个项目重点是对串扰进行建模和缓解，从去卷积方法开始，逐步发展到对耦合泊松过程进行更复杂的建模。有了这些新的模型和方法，该项目将开发几项成像创新。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Light has a fundamental smallest quantity - a photon - that is very far from everyday human experience. For example, the number of photons collected by the camera in a mobile phone to form a typical photograph is in the trillions. Nevertheless, there are some increasingly common devices that rely on measuring light down to the smallest possible amounts. These are devices that perform "single-photon detection" (SPD). SPD is used in combination with pulsed lasers in the 3D imaging systems of self-driving cars, and it is used to enable augmented reality in devices like the iPad Pro. Many types of scientific imaging also use SPD to see individual molecules, track proteins, or determine chemical concentrations through spectroscopy. Devices for SPD have complicated behavior that is often modeled naively. Using more detailed modeling, this project will develop data processing methods to use with SPD to improve various systems.Though SPD is on the verge of everyday use, signal processing for SPD has lagged far behind. Both system design and device design are guided by trade-offs, and those tradeoffs depend greatly on the sophistication of the data processing. Therefore, novel signal processing will not only improve applications that use SPD, it will also influence hardware designs. Devices with single-photon sensitivity suffer a limitation by which each detection event causes a non-zero "dead time" during which the system is unable to register incident particles. Since signal processing methods to mitigate dead time effects are few and not well known, it is customary to carefully avoid dead time effects by operating systems such that photons very rarely arrive during dead times. While this indeed makes the effect of dead time negligible, one may naturally ask whether this is a good practice. Preliminary results suggest that allowing appreciable dead time effects and compensating for them can provide dramatic improvements in lidar. A project focus is to improve and exploit modeling of dead time effects to create the most informative measurements for lidar and other applications. In arrays for SPD, maintaining low crosstalk is a major barrier to increasing fill factor and thus increasing detection efficiency. Another project focus is to model and mitigate crosstalk, starting with a deconvolution approach and progressing to more sophisticated modeling of coupled Poisson processes. With these novel models and methods, the project will develop several imaging innovations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.