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Record W2336872402 · doi:10.1149/ma2014-01/40/1484

Single Photon Avalanche Diode Imaging Systems for Biomedical Applications

2014· article· en· W2336872402 on OpenAlexaff
Darek Palubiak, M. Jamal Deen

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhotomultiplierPhysicsAvalanche photodiodePhotonCMOSDetectorPhotonicsOpticsOptoelectronicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Ever since the discovery of the photon by Einstein and Planck, scientists have continually strived to achieve single photon imaging; considered the holy grail of photosensing, since it represents the ultimate detection and precision limit of electromagnetic radiation. With the invention of the photomultiplier tube (PMT) in the 1930s, single-photon detection became feasible. Since then, PMTs have been developed to meet a wide range of application needs. For the most demanding time-resolved measurements, micro-channel plate (MCP) PMTs have been used. However, PMT devices are inherently limited due to their large size, high-cost, fragility, high operating voltages, and magnetic field susceptibility. On the other hand, Geiger-mode APDs realized in CMOS technology do not suffer from these limitations, and in recent years, their performance has significantly improved so they are now comparable to PMTs. As such, single photon imagers based on CMOS single-photon avalanche detectors (SPAD) have been realized for applications where speed, cost, miniaturization and power consumption are critical [1]. The main components of a high-speed single photon imaging system is the SPAD, which converts a single photon to an electrical pulse, the SPAD front-end circuit, which shapes the pulse and controls the SPAD, and the time-to-digital converter (TDC), which converts the time interval between the detected photon and a reference pulse, or the time between two detected photons, into a digital code. Deep sub-micron (DSM) CMOS technology lends itself very well to the realization of such a system; all the components can be integrated together with very high reliability at a very low cost, digital processing can be readily applied to the detected photons, and dense arrays can be realized for multi-dimensional processing. Over the last decade, CMOS single-photon imaging systems have been applied in the fields of biomedical research, astronomy, nuclear physics, ranging and communications [2], [3], [4]. This work describes the design of the main components of a high-speed single photon imaging system for biomedical applications, implemented in a standard, commercial CMOS 0.13 μm technology from IBM. A passively-quenched SPAD pixel was characterized in terms of its noise, temporal resolution, efficiency, and temperature performance. By fabricating the SPAD in a DSM technology, we were able to take advantage of the inherent speed, lower parasitic capacitance, and increased integration density afforded by the CMOS process. Indeed, a dead-time down to 20 ns was achievable with the SPAD front-end circuit, since the total capacitance of the 9.6 x 8.5 μm 2 SPAD remained below 100 fF. Also, a new dual-interpolating tapped-delay-line TDC prototype chip was designed and evaluated for a future integration with an SPAD in a multichannel fluorescent lifetime imaging / positron emission tomography (FLIM/PET) system. The design goals were to achieve simultaneously a high-resolution, high throughput, good linearity, wide measurement range, small size and low power. The 8-bit TDC chip measures time intervals with a 156 ns least-significant bit (LSB) between a common STOP pulse from a laser and a START pulse generated by an SPAD, as in a typical time-correlated single-photon counting (TCSPC) acquisition chain shown in figure 1. In this presentation, details of the key design issues and the results from extensive testing will be provided and discussed. References [1] Cova, S.D.; Ghioni, M., IEEE Photon. J. , vol.3, no.2, pp.274-277, April 2011 [2] Markovic, E.; Tisa, S.; Tosi, A.; Zappa, F., Proc. SPIE , vol. 8033, no. 80330A, 2011 [3] Maruyama, Y.; Blacksberg, J.; Charbon, E., Proc. IEEE Int. Solid-State Circuits Conf., pp.110-111, 2013 [4] Fisher, E.; Underwood, I.; Henderson, R., IEEE J. Solid-State Circuits , vol.48, no.7, pp.1638-1650, 2013

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.656
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.252
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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