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Record W1970075513 · doi:10.1109/newcas.2012.6329027

Premature edge breakdown prevention techniques in CMOS APD fabrication

2012· article· en· W1970075513 on OpenAlexafffund
Ehsan Kamrani, Frédéric Lesage, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsPolytechnique Montréal
FundersCanadian Institutes of Health ResearchHealth CanadaCanada Research Chairs
KeywordsAvalanche photodiodeMiniaturizationFabricationCMOSGuard (computer science)Enhanced Data Rates for GSM EvolutionElectronic engineeringDetectorMaterials scienceComputer scienceOptoelectronicsEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper we have introduced the most popular applied premature edge breakdown prevention (PEBP) techniques and proposed a new practical and efficient design procedure technique to design a functional avalanche photodiode using standard CMOS process based on our design, simulation and fabrication experiences. The device simulations are used to find the best dimensional values minimizing PEB. Three proposed PEBP techniques are emerged from a systematic study aimed at miniaturization, while optimizing the overall performance. Based on the experimental results gained from the fabrication of a p-well and p-sub guard-rings a new n-well guard-ring PEBP technique is introduced and its performance is evaluated using the device simulation. It exhibits a dark count rate of 1 kHz (with 0.5V excess bias at room temperature), a maximum photon detection probability of 70% at maximum excess bias and 9V breakdown voltage.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.279
Teacher spread0.268 · 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".

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Citations14
Published2012
Admission routes2
Has abstractyes

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