MétaCan
Menu
Back to cohort
Record W2077892813 · doi:10.1117/12.681189

Modeling of avalanche photodiodes by Crosslight APSYS

2006· article· en· W2077892813 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 · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsCrosslight Software (Canada)
Fundersnot available
KeywordsAvalanche photodiodeAPDSDark currentOptoelectronicsComputer sciencePhotodiodeBreakdown voltageSingle-photon avalanche diodeBand diagramElectronic engineeringVoltageMaterials scienceElectrical engineeringPhotodetectorEngineeringDetectorTelecommunicationsBand gap

Abstract

fetched live from OpenAlex

Avalanche photodiodes (APDs) are being widely utilized in various application fields where a compact technology computer aided design (TCAD) kit capable for APD modeling is highly demanded. In this work, based on the advanced drift and diffusion model with commercial software, the Crosslight APSYS, avalanche photodiodes, especially the InP/InGaAs separate absorption, grading, charge and multiplication (SAGCM) APDs for high bit-rate operation have been modeled. Basic physical quantities like band diagram, optical absorption and generation are calculated. Performance characteristics such as dark- and photo-current, photoresponsivity/multiplication gain, breakdown voltage, excess noise, frequency response and bandwidth etc., are simulated. The modeling results are selectively presented, analyzed, and some of results are compared with the experimental. Device design optimization issues are further discussed with respect to the applicable features of the Crosslight APSYS within the framework of drift-diffusion theory.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
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)0.0020.000

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Optical Sensing TechnologiesFrench-language works237,207