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The impact of etched trenches geometry and dielectric material on the electrical behaviour of silicon-on-insulator self-switching diodes

2011· article· en· W2141147935 on OpenAlexafffund
G. Farhi, Denis Morris, Serge A. Charlebois, J-P. Raskin

Bibliographic record

VenueNanotechnology · 2011
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersCMC Microsystems
KeywordsMaterials scienceTrenchInsulator (electricity)DielectricDiodeOptoelectronicsThermal conductionSiliconBiasingTransverse planeDopingSubstrate (aquarium)VoltageElectrical engineeringNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Hole electrical transport in a p-doped nanochannel defined between two L-shape etched trenches made on a silicon-on-insulator substrate is investigated using a TCAD-Medici simulator. We study the impact of the etched trenches' geometry and dielectric filling materials on the current-voltage characteristics of the device. Carrier accumulation on frontiers defined by the trenches causes a modulation of the hole density inside the conduction channel as the bias voltage varies and this gives rise to a diode-like characteristic. For a 1.2 µm-long channel, plots of the electric field distribution show that a nonlinear transport regime is reached at a moderate reverse and forward bias of ± 2 V. Plots of the carrier velocity along the conduction channel show that holes remain hot for a few hundreds of nm outside the nanometre-wide channel, at a bias of ± 10 V. Filling the etched trenches with a high-κ dielectric material gives rise to a lower threshold voltage, V(th). A similar decrease of V(th) is also achieved by reducing the longitudinal and/or the transverse trench width. Our simulation results provide useful design guidelines for future integrated self-switching-diode-based circuits.

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 score0.017
Threshold uncertainty score0.373

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 head0.224
Teacher spread0.210 · 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

Citations19
Published2011
Admission routes2
Has abstractyes

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