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Record W385597656 · doi:10.1063/1.4921798

Coupled dopant diffusion and segregation in inhomogeneous SiGe alloys: Experiments and modeling

2015· article· en· W385597656 on OpenAlexaff
Yiheng Lin, Hiroshi Yasuda, Manfred Schiekofer, Guangrui Xia

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsUniversity of British Columbia
FundersSemiconductor Research Corporation
KeywordsDopantMaterials scienceHeterojunctionDiffusionBipolar junction transistorLattice (music)Condensed matter physicsChemical physicsDopingThermodynamicsTransistorOptoelectronicsChemistryPhysics

Abstract

fetched live from OpenAlex

A coupled diffusion and segregation model was derived, where the contributions from diffusion and segregation to dopant flux are explicitly shown. The model is generic to coupled diffusion and segregation in inhomogeneous alloys, and provides a new approach in segregation coefficient extraction, which is especially helpful for heterostructures with lattice mismatch strains. Experiments of coupled P diffusion and segregation were performed with graded SiGe layers for Ge molar fractions up to 0.18, which are relevant to pnp SiGe heterojunction bipolar transistors. The model was shown to describe both diffusion and segregation behavior well. The diffusion-segregation model for P in SiGe alloys was calibrated and Eseg=0.5 eV is suggested for the temperature range from 800 °C to 950 °C.

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 score0.436
Threshold uncertainty score0.292

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 head0.031
GPT teacher head0.237
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

Citations4
Published2015
Admission routes1
Has abstractyes

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