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Record W3213675067 · doi:10.1109/jphot.2021.3128055

Improved Noise Performance of CMOS Poly Gate Single-Photon Avalanche Diodes

2021· article· en· W3213675067 on OpenAlexafffund
Wei Jiang, Ryan P. Scott, M. Jamal Deen

Bibliographic record

VenueIEEE photonics journal · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsPhysicsDiodeOptoelectronicsCMOSNoise (video)Electrical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

The noise performance of three types of n+/p-well single-photon avalanche diodes (SPADs) fabricated in a standard 180 nm CMOS technology is studied. The SPADs had different poly gate configurations: no poly gate (SPAD_NG), a dummy floating poly gate (SPAD_DG), and a field poly gate connected to the n+cathode (SPAD_FG). The measurement results of dark count rate and afterpulsing showed that the SPAD_DG had better noise performance compared to the SPAD_NG. This is because the dummy poly gate pushed the shallow trench isolation away from the active region of the SPAD, thus reducing the dark noise generated from the Si-SiO2interface. The measurement results also revealed that the noise performance can be further improved by connecting the poly gate to the n+cathode. The voltage on the poly gate in SPAD_FG reduced the electric field in the n-well guard ring (GR) region, thus reducing the carriers from the GR region that can enter the active region of SPADs and initiate dark counts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations27
Published2021
Admission routes2
Has abstractyes

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