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Record W2963543486 · doi:10.1103/physrevb.72.115331

Origin of switching noise in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi mathvariant="normal">Ga</mml:mi><mml:mi mathvariant="normal">As</mml:mi><mml:mo>∕</mml:mo><mml:msub><mml:mi mathvariant="normal">Al</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mi mathvariant="normal">Ga</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mi mathvariant="normal">As</mml:mi></mml:mrow></mml:math>lateral gated devices

2005· article· lv· W2963543486 on OpenAlexaff
Michel Pioro-Ladrière, J. H. Davies, A.R. Long, Andrew Sachrajda, Louis Gaudreau, P. Zawadzki, J. Lapointe, J. A. Gupta, Z. R. Wasilewski, Sergei Studenikin

Bibliographic record

VenuePhysical Review B · 2005
Typearticle
Languagelv
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsInstitute for Microstructural SciencesRegroupement Québécois sur les Matériaux de PointeUniversité de Sherbrooke
Fundersnot available
KeywordsNoise (video)Computer scienceMathematicsAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

We have studied switching (telegraph) noise at low temperature in $\mathrm{Ga}\mathrm{As}∕{\mathrm{Al}}_{x}{\mathrm{Ga}}_{1\ensuremath{-}x}\mathrm{As}$ heterostructures with lateral gates and introduced a model for its origin, which explains why noise can be suppressed by cooling samples with a positive bias on the gates. The noise was measured by monitoring the conductance fluctuations around ${e}^{2}∕h$ on the first step of a quantum point contact at around $1.2\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. Cooling with a positive bias on the gates dramatically reduces this noise, while an asymmetric bias exacerbates it. Our model is that the noise originates from a leakage current of electrons that tunnel through the Schottky barrier under the gate into the conduction band and become trapped near the active region of the device. The key to reducing noise is to keep the barrier opaque under experimental conditions. Cooling with a positive bias on the gates reduces the density of ionized donors. This builds in an effective negative gate voltage so that a smaller negative bias is needed to reach the desired operating point. This suppresses tunneling from the gate and hence the noise. The reduction in the density of ionized donors also strengthens the barrier to tunneling at a given applied voltage. Further support for the model comes from our direct observation of the leakage current into a closed quantum dot, around ${10}^{\ensuremath{-}20}\phantom{\rule{0.3em}{0ex}}\mathrm{A}$ for this device. The current was detected by a neighboring quantum point contact, which showed monotonic steps in time associated with the tunneling of single electrons into the dot. If asymmetric gate voltages are applied, our model suggests that the noise will increase as a consequence of the more negative gate voltage applied to one of the gates to maintain the same device conductance. We observe exactly this behavior in our experiments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations139
Published2005
Admission routes1
Has abstractyes

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