Single-Photon Avalanche Diode (SPAD) sensors are one of the detectors of choice in LIDAR applications, due to their high sensitivity and time resolution. Traditionally, single-point SPAD detectors have been used, necessitating optical scanning, and hence leading to slower acquisition times. However, recent advances in SPAD technology have yielded high pixel count imagers. In these sensors, high sensitivity is ensured by maximising the photo-sensitive area of the device, whilst time-resolved capability is offered by the time stamping of photon detections and/or a programmable timegate synced to the laser source. The resulting sensors show an exciting promise for LIDAR, especially in challenging, low-light, high-speed applications.
Proc. SPIE. 10504, Biophysics, Biology and Biophotonics III: the Crossroads
KEYWORDS: CMOS sensors, Sensors, Spectroscopy, Luminescence, Molecules, Single photon, Time correlated photon counting, Time resolved spectroscopy, Fluorescence resonance energy transfer, Molecular energy transfer
We demonstrate a new 512x16 single photon avalanche diode (SPAD) based line sensor with per-pixel TCSPC histogramming for time-resolved, time-zoomable, FRET spectroscopy. The line sensor can operate in single photon counting (SPC) mode as well as time-correlated single photon counting (TCSPC) and per-pixel histogramming modes. TCSPC has been the preferred method for fluorescence lifetime measurements due to its collection of full decays as a histogram of arrival times. However, TCSPC is slow due to only capturing one photon per exposure and large timestamp data transfer requirements for offline histogramming. On-chip histogramming improves the data rate by allowing multiple SPAD pulses (up to one pulse per laser period) to be processed in each exposure cycle, along with secondly reducing the I/O bottleneck as only the final histogram is transferred. This can enable 50x higher acquisition rates (up to 10 billion counts per second), along with time-zoomable histogramming operation from 1.6ns to 205ns with 50ps resolution. A broad spectral range can be interrogated with the sensor (450-900nm). Overall, these sensors provide a unique combination of light sensing capabilities for use in high speed, sensitive, optical instrumentation in the time/wavelength domain. We test the sensor performance by observation of fluorescence resonance energy transfer (FRET) between FAM and TAMRA and between EGFP and RFP FRET standards.
Time-resolved spectroscopy in the presence of noise is challenging. We have developed a new 512 pixel line sensor with 16 single-photon-avalanche (SPAD) detectors per pixel and ultrafast in-pixel time-correlated single photon counting (TCSPC) histogramming for such applications. SPADs are near shot noise limited detectors but we are still faced with the problem of high dark count rate (DCR) SPADs. The noisiest SPADs can be switched off to optimise signal-to-noiseratios (SNR) at the expense of longer acquisition/exposure times than would be possible if more SPADs were exploited. Here we present detailed noise characterization of our array. We build a DCR map for the sensor and demonstrate the effect of switching off the noisiest SPADs in each pixel. 24% percent of SPADs in the array are measured to have DCR in excess of 1kHz, while the best SPAD selection per pixel reduces DCR to 53+/-7Hz across the entire array. We demonstrate that selection of the lowest DCR SPAD in each pixel leads to the emergence of sparse spatial sampling noise in the sensor.
The SPADnet FP7 European project is aimed at a new generation of fully digital, scalable and networked photonic components to enable large area image sensors, with primary target gamma-ray and coincidence detection in (Time-of- Flight) Positron Emission Tomography (PET). SPADnet relies on standard CMOS technology, therefore allowing for MRI compatibility. SPADnet innovates in several areas of PET systems, from optical coupling to single-photon sensor architectures, from intelligent ring networks to reconstruction algorithms. It is built around a natively digital, intelligent SPAD (Single-Photon Avalanche Diode)-based sensor device which comprises an array of 8×16 pixels, each composed of 4 mini-SiPMs with in situ time-to-digital conversion, a multi-ring network to filter, carry, and process data produced by the sensors at 2Gbps, and a 130nm CMOS process enabling mass-production of photonic modules that are optically interfaced to scintillator crystals. A few tens of sensor devices are tightly abutted on a single PCB to form a so-called sensor tile, thanks to TSV (Through Silicon Via) connections to their backside (replacing conventional wire bonding). The sensor tile is in turn interfaced to an FPGA-based PCB on its back. The resulting photonic module acts as an autonomous sensing and computing unit, individually detecting gamma photons as well as thermal and Compton events. It determines in real time basic information for each scintillation event, such as exact time of arrival, position and energy, and communicates it to its peers in the field of view. Coincidence detection does therefore occur directly in the ring itself, in a differed and distributed manner to ensure scalability. The selected true coincidence events are then collected by a snooper module, from which they are transferred to an external reconstruction computer using Gigabit Ethernet.
The rapid advancements in ad hoc sensor networks, MEMS (micro-electro-mechanical systems) devices, low-power
electronics, adaptive hardware and systems (AHS), reconfigurable architectures, high-performance computing platforms,
distributed operating systems, micro-spacecrafts, and micro-sensors have enabled the design and development of a highperformance
satellite sensor network (SSN). Due to the changing environment and the varying missions that a SSN may
have, there is an increasing need to develop efficient strategies to design, operate, and manage the system at different
levels from an individual satellite node to the whole network. Towards this end, this paper presents an adaptive
approach to space-based picosatellite sensor network by exploiting efficient bio-inspired optimization algorithms,
particularly for solving multi-objective optimization problems at both local (node) and global (network) system levels.
The proposed approach can be hierarchically used for dealing with the challenging optimization problems arising from
the energy-constrained satellite sensor networks. Simulation results are provided to demonstrate the effectiveness of the
proposed approach through its application in solving both node-level and system-level optimization problems.