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Record W4224238556 · doi:10.1063/5.0089345

Perspective on phase-controlled currents in semiconductors driven by structured light

2022· article· en· W4224238556 on OpenAlexafffund
Shawn Sederberg, P. B. Corkum

Bibliographic record

VenueApplied Physics Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsJoint Attosecond Science LaboratoryNational Research Council CanadaSimon Fraser UniversityUniversity of Ottawa
FundersArmy Research OfficeDefense Advanced Research Projects AgencyCanada Research Chairs
KeywordsCoherence (philosophical gambling strategy)Coherent controlLaserPhysicsElectronicsElectronCoherent spectroscopyQuantum opticsQuasiparticleQuantumOpticsSemiconductorQuantum mechanicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Controlling electrons with ever-greater precision is central to both classical and quantum electronics. Since the invention of the laser, virtually every property of coherent light has been tamed, making it one of the most precise tools available to science, technology, and medicine. Coherent control involves the transduction of an exquisitely defined property of light to an electronic system, imparting coherence to an attribute of its constituent electrons. Early developments in coherent control utilized Gaussian laser beams and spatially averaged measurements. The spatial structure and orbital angular momentum of laser light provide additional degrees of freedom for steering electronic and quasiparticle excitations in condensed matter systems. In this Perspective, we first introduce the concept of coherent control in semiconductors. We then proceed to discuss the application of structured light beams to coherent control and the requirement for spatially resolved current detection. Subsequently, we present an overview of recent experiments that were performed using cylindrical vector beams and laser beams with structured phase fronts. Finally, we provide an outlook on the horizons that have emerged with these developments and future directions of interest.

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 score0.121
Threshold uncertainty score0.953

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)0.0010.000

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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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