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Record W2030039214 · doi:10.1143/jjap.49.04dj03

Slow and Fast Electron Channels in a Coherent Quantum Dot Mixer

2010· article· en· W2030039214 on OpenAlexaff
Guy Austing, C. Payette, Guolin Yu, James A. Gupta, G. C. Aers, Selva Nair, S. Amaha, Seigo Tarucha

Bibliographic record

VenueJapanese Journal of Applied Physics · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of TorontoMcGill UniversityInstitute for Microstructural Sciences
Fundersnot available
KeywordsQuantum dotQuantum tunnellingPhysicsCoulomb blockadeElectronSuperposition principleDwell timeMixing (physics)Condensed matter physicsAtomic physicsResonance (particle physics)Molecular physicsQuantum mechanicsVoltage

Abstract

fetched live from OpenAlex

We describe a means to realize slow and fast electron channels by coherent mixing of single-particle levels in quantum dots. The underlying physics, which gives insight into state superposition, can potentially be realized in multi-dot structures with complex gate control. However, we employ vertical double dot structures and in our scheme the mixing of single-particle levels arises because of natural perturbations in the confining potential of the high-symmetry dots. Additionally, because of the intrinsic properties of a Fock–Darwin-like spectrum, we utilize a magnetic field to bring multiple single-particle energy levels into close proximity. We determine single-electron resonant tunneling times (effectively dwell times when on resonance) that are either extended in the slow channel or shortened in the fast channel. Most dramatically, for the slow channel, slow-down factors of ∼10 and single-electron resonant tunneling times extended into the µs range are demonstrated in all systems of two, three, and four mixed single-particle states investigated here.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.675

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 integrity0.0000.001
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 head0.218
Teacher spread0.212 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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