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Record W1996663702 · doi:10.1117/12.795549

Suppressing premature edge breakdown for InP/InGaAs avalanche photodiodes by modeling analyses

2008· article· en· W1996663702 on OpenAlexaff
Yuanzhang Xiao, Z. Q. Li, Z. M. Simon Li

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsCrosslight Software (Canada)
Fundersnot available
KeywordsAvalanche photodiodeOptoelectronicsPhotocurrentBreakdown voltageMaterials scienceSingle-photon avalanche diodeAvalanche diodeBand diagramAvalanche breakdownElectric fieldPhotodiodeOpticsVoltageBand gapElectrical engineeringPhysicsDetector

Abstract

fetched live from OpenAlex

In this work, based on the advanced drift and diffusion model with commercial software, the Crosslight APSYS, twodimensional photoresponsivity behavior for the InP/InGaAs separate absorption, grading, charge and multiplication avalanche photodiodes have been modeled to analyze suppressing premature edge breakdown. Basic physical quantities like band diagram, photon absorption, carrier generation and electric field as well as performance characteristics such as photocurrent, multiplication gain, and breakdown voltage etc., are obtained and selectively presented. Modeling results indicate that an etched mesa structure with the charge sheet layer can effectively suppress the premature edge breakdown in the device periphery region. Optimization modeling results with mesa step height are also demonstrated. Approach to model complex guard ring structure with double diffusion is further explored. Possible combination of Crosslight CSuprem diffusion profile is also 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations2
Published2008
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSemiconductor Quantum Structures and DevicesFrench-language works237,207