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Record W2060073374 · doi:10.1117/12.497403

Physical model for low-frequency noise in avalanche breakdown of PN junctions

2003· article· en· W2060073374 on OpenAlexafffund
Ognian Marinov, M. Jamal Deen

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroplasmaNoise (video)Impact ionizationAvalanche breakdownMaterials scienceOptoelectronicsSpiceAvalanche diodeSingle-photon avalanche diodeDiodeDepletion regionSpace chargePlasmaIonizationComputational physicsElectrical engineeringPhysicsElectronic engineeringBreakdown voltageAvalanche photodiodeComputer scienceVoltageOpticsEngineeringSemiconductorIon

Abstract

fetched live from OpenAlex

A physically-based transient microplasma based model for low frequency noise in pn diodes is discussed and implemented in SPICE simulator. The simulation indicates that the model correctly describes the non-monotonic behavior of both the DC and the noise characteristics of diode at the onset of avalanche breakdown. Since the model is based on a new microplasma switching theory, the results of simulation confirm the findings of this theory. These are, as follows for the microplasma. Its switching threshold is the condition of equality of free- to space charge concentration in depletion layer. Its on-current is approximately twice the threshold current. It is initialized by the charge generation due to few recombination centers in microplasma region at high avalanche multiplication due to impact ionization, while the microplasma turn-off is due to carrier diffusion from microplasma region into the depletion layer at low, but larger than 1, avalanche multiplication.

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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.221
Teacher spread0.213 · 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

Citations0
Published2003
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicElectrostatic Discharge in ElectronicsFrench-language works237,207