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Impact of silicide layer on single photon avalanche diodes in a 130 nm CMOS process

2016· article· en· W2499871158 on OpenAlexafffund
Cheng Zeng, Darek Palubiak, Xiaoqing Zheng, M. Jamal Deen, Hao Peng

Bibliographic record

VenueJournal of Physics D Applied Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsMcMaster University
FundersFedDev OntarioCanada Research ChairsCMC Microsystems
KeywordsSilicideOptoelectronicsMaterials scienceDiodeSingle-photon avalanche diodeLayer (electronics)CMOSAvalanche diodeProcess (computing)OpticsNanotechnologyAvalanche photodiodeElectrical engineeringSiliconComputer scienceEngineeringPhysicsDetectorBreakdown voltage

Abstract

fetched live from OpenAlex

Abstract Single photon avalanche diode (SPAD) is an attractive solid-state optical detector that offers ultra-high photon sensitivity (down to the single photon level), high speed (sub-nanosecond dead time) and good timing performance (less than 100 ps). In this work, the impact of the silicide layer on SPAD’s characteristics, including the breakdown voltage, dark count rate (DCR), after-pulsing probability and photon detection efficiency (PDE) is investigated. For this purpose, two sets of SPAD structures in a standard 130 nm complementary metal oxide semiconductor (CMOS) process are designed, fabricated, measured and compared. A factor of 4.5 (minimum) in DCR reduction, and 5 in PDE improvements are observed when the silicide layer is removed from the SPAD structure. However, the after-pulsing probability of the SPAD without silicide layer is two times higher than its counterpart with silicide. The reasons for these changes will be discussed.

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 score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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