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Record W1978444407 · doi:10.1142/s0219477504001896

PHYSICAL MODEL FOR LOW FREQUENCY NOISE IN AVALANCHE BREAKDOWN OF PN JUNCTIONS

2004· article· en· W1978444407 on OpenAlexafffund
Ognian Marinov, M. Jamal Deen

Bibliographic record

VenueFluctuation and Noise Letters · 2004
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroplasmaImpact ionizationNoise (video)Materials scienceOptoelectronicsIonizationAvalanche breakdownPlasmaDiffusionDiodeSpiceSingle-photon avalanche diodeComputational physicsAtomic physicsPhysicsElectrical engineeringBreakdown voltageAvalanche photodiodeOpticsComputer scienceVoltageIonEngineering

Abstract

fetched live from OpenAlex

A physically-based transient model for low frequency noise of both microplasma and impact ionization in PN diodes is discussed and implemented in the 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. The model is based on a new microplasma switching theory, and the results of simulation confirm the findings of this theory. The microplasma switching threshold is the condition of equality of free- to space charge concentration in the depletion layer. The microplasma turn-on is initialized by the charge generation due to few recombination centers in the microplasma region at high avalanche multiplication due to impact ionization. The microplasma on-current is approximately twice the threshold current and the on-current sustains until the low, but larger than 1, avalanche multiplication compensates for the carrier diffusion from microplasma region into the depletion layer. When the multiplication becomes lower than the diffusion, the microplasma switches off.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.319
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.213
Teacher spread0.205 · 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 teacher head, 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

Citations7
Published2004
Admission routes2
Has abstractyes

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