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Record W2102121495 · doi:10.1139/p03-053

A simple formulation of the saturation current density in heavily doped emitters

2003· article· en· W2102121495 on OpenAlexvenueno aff
Alaeddine Zouari, Adel Ben Arab

Bibliographic record

VenueCanadian Journal of Physics · 2003
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsCommon emitterSaturation currentCurrent densityDopingSaturation (graph theory)Current (fluid)DiffusionSimple (philosophy)Computational physicsCondensed matter physicsAtomic physicsMathematical analysisQuantum mechanicsThermodynamicsOptoelectronicsMathematicsCombinatoricsVoltage

Abstract

fetched live from OpenAlex

In a non-uniformly and heavily doped emitter region of a bipolar transistor, the continuity equation and the minority-current equation cannot be solved exactly in closed form. This paper shows that the calculation of minority-carrier current density can be calculated by a simple approach. This approach is based on the average value of the equilibrium hole density p 0 , diffusion constant D p , and lifetime τ p of minority carriers and leads to two coupled differential equations of the first order. These equations can be solved easily and can give a simple expression for the current density. Three definitions of the average values of p 0 , D p , and τ p are used and lead to three expressions for the emitter current density. The latter is identical to the one established by Rinaldi using another mathematical analysis and gives very accurate results for a shallow emitter (W < 1 µm), irrespective of the range peak doping level N(W) and surface-recombination velocity S. On the other hand, the other two expressions lead also to accurate results for the current density depending on the value of the surface-recombination velocity, but cannot be used when N(W) is greater than 10 20 cm –3 and W is superior to 0.1 µm. PACS Nos.: 72.10.–d, 72.20.–i

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.308

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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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 designObservational
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

Citations11
Published2003
Admission routes1
Has abstractyes

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