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Record W2890419265 · doi:10.1109/ted.2018.2869024

Theoretical Evaluation of the Effects of Isolation-Feature Size and Geometry on the Built-In Strain and 2-D Electron Gas Density of AlGaN/GaN Heterostructures

2018· article· en· W2890419265 on OpenAlexafffund
J. R. Gosselin, Pouya Valizadeh

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceHeterojunctionWurtzite crystal structureEnergy minimizationOptoelectronicsGallium nitrideTransistorVoltageNanotechnologyElectrical engineeringChemistryEngineeringComputational chemistry

Abstract

fetched live from OpenAlex

Using a commercial self-consistent Poisson-Schrödinger solver with the built-in possibility of allowing elastic energy minimization, the strain and the sheet charge density induced at the pseudomorphically grown Ga-face Wurtzite AlGaN/GaN heterojunctions are evaluated in the context of 3-D simulation of heterostructure field-effect transistor (HFET) epilayers etched into a variety of isolation-feature sizes and geometries. Through these studies and in the presence of surface states, the extent of the relevance of strain minimization in the vicinity of the unconstrained boundaries of isolation features of different degrees of roundness and perimeter-to-area ratio to threshold-voltage engineering is assessed. Although it is demonstrated that threshold-voltage shift caused by this induced strain minimization is smaller than the amount of shift levied by the depleting effect of the negatively charged states on the sidewall facets, it is shown that the reduction of the isolation-feature size is capable of substantially reducing the average trace of the stress tensor across such heterointerfaces. Considering the importance of this factor to the long-term reliability of AlGaN/GaN HFETs, especially when undergoing self-heating at high-power levels, use of alternative isolation features such as small islands of a lateral area less than 1000 nm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is proposed as a solution.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.395

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 head0.006
GPT teacher head0.256
Teacher spread0.250 · 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 designBench or experimental
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
Published2018
Admission routes2
Has abstractyes

Explore more

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