CCSS: Signal Processing for Single-Photon Detectors

CCSS：单光子探测器的信号处理

• 批准号: 2039762
• 负责人: Vivek Goyal
• 金额: $ 38万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039762&HistoricalAwards=false
• 关键词: CCSS; Signal; Processing; Single; Photon

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.

光有一个基本的最小量--光子--这与人类的日常经验相去甚远。例如，由移动的电话中的照相机收集以形成典型照片的光子的数量以万亿计。然而，有一些越来越常见的设备依赖于测量尽可能小的光。这些是执行“单光子探测”（SPD）的设备。SPD在自动驾驶汽车的3D成像系统中与脉冲激光结合使用，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确定化学浓度。SPD设备具有复杂的行为，通常是简单建模的。通过更详细的建模，本项目将开发与SPD一起使用的数据处理方法，以改进各种系统。虽然SPD即将进入日常使用，但SPD的信号处理却远远落后。系统设计和设备设计都受到权衡的指导，这些权衡在很大程度上取决于数据处理的复杂性。因此，新的信号处理不仅会改善使用SPD的应用，还会影响硬件设计。具有单光子灵敏度的装置受到限制，通过该限制，每个检测事件引起非零“死区时间”，在该死区时间期间系统不能记录入射粒子。由于用于减轻死区时间效应的信号处理方法很少并且不为人所知，因此通常通过操作系统小心地避免死区时间效应，使得光子很少在死区时间期间到达。虽然这确实使死区时间的影响可以忽略不计，但人们可能会自然地问这是否是一种好的做法。初步结果表明，允许可观的死区时间的影响和补偿他们可以提供显着的改善激光雷达。项目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创建最具信息量的测量。在SPD的阵列中，保持低串扰是增加填充因子从而增加检测效率的主要障碍。另一个项目的重点是建模和减轻串扰，从去卷积方法开始，并进展到耦合泊松过程的更复杂的建模。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Continuous-Time Modeling and Analysis of Particle Beam Metrology

• DOI: 10.1109/jsait.2023.3283911
• 发表时间: 2023-03
• 期刊: IEEE Journal on Selected Areas in Information Theory
• 作者: A. Agarwal;Minxu Peng;V. Goyal

Denoising Particle Beam Micrographs With Plug-and-Play Methods

• DOI: 10.1109/tci.2023.3282042
• 发表时间: 2022-08
• 期刊: IEEE Transactions on Computational Imaging
• 影响因子: 5.4
• 作者: Minxu Peng;Ruangrawee Kitichotkul;Sheila W. Seidel;Christopher C. Yu;V. Goyal