MétaCan
Menu
Back to cohort
Record W4213060091 · doi:10.1116/6.0001650

Negative differential resistance in photoassisted field emission from Si nanowires

2022· article· en· W4213060091 on OpenAlexaff
M. Choueib, Arnaud Derouet, P. Vincent, A. Ayari, S. Perisanu, P. Poncharal, Costel‐Sorin Cojocaru, Richard Martel, S. T. Purcell

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2022
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMaterials scienceNanowireField electron emissionQuantum tunnellingOptoelectronicsSaturation (graph theory)ElectronSpace chargeSiliconDepletion regionBrightnessNanotechnologySemiconductorOpticsPhysics

Abstract

fetched live from OpenAlex

Field emission (FE) from semiconducting nanowires (NWs) is studied for expanding electron gun performances and functionality in terms of stability, brightness, and pulsed emission. Here, we report on a pronounced and robust double negative differential resistance (NDR) in the FE IV characteristics measured during photoassisted field emission experiments on highly crystalline p-type silicon NWs. The main feature is a double NDR in the current saturation regime, which can be modulated by both temperature and light intensity. These results contrast with previous FE studies in which only a barely noticeable single NDR was reported. Several mechanisms for the physical explanation of the NDR are currently under consideration: photogenerated carrier instabilities in the depletion region, which give rise to a pulsed space-charge current in the nanowire or tunneling through a double quantum well formed by confinement at the NW apex. Because NDRs are signatures of pulsed currents, these results suggest new functionalities for which pulsed electron sources can potentially be achieved at high repetition rates.

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)0.001
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 score0.015
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and PhenomenaSame topicSemiconductor materials and devicesFrench-language works237,207