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Record W4388293830 · doi:10.1063/5.0171614

Physics of photovoltaic signal modifications in p–n photodiodes

2023· article· en· W4388293830 on OpenAlexaff
Mingrui Yuan, Michael K. Rafailov, R. Binder

Bibliographic record

VenueJournal of Applied Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsUltrashort pulsePhotodiodeExcitationPhotovoltaic effectOptoelectronicsSIGNAL (programming language)DetectorNanosecondMaterials sciencePhotovoltaic systemOpticsAtomic physicsLaserElectrical engineering

Abstract

fetched live from OpenAlex

The photovoltaic signal is an important characteristic of photodetectors, including but not limited to those that are based on p–n or p–i–n photodiodes. In an open-circuit configuration, pulsed excitation of the detector with ultrafast (femto or nanosecond) pulses leads to a photovoltaic signal that decays slowly (micro-second time scale). If the physics in the detector is dominated by the recombination of the photo-excited charge carriers, one expects the signal to decay without changing its sign. However, some experiments using short-pulse excitation have found that photovoltaic signals can undergo a sign change as a function of time following excitation, with positive signals immediately following the excitation, turning to negative signals several microseconds later. Here, we study various physical effects (density, temperature, electrostriction, pressure, photostriction, and bandgap renormalization) and determine their effect on photovoltaic signals. If, following ultrafast excitation, the carrier density and temperature are increased, and during relaxation the system reaches a state sufficiently close to the quasi-thermal equilibrium in which the carrier density is still elevated, but smaller than the intrinsic thermal equilibrium density at the elevated temperature, then the signal can become negative.

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 score0.313
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.269
Teacher spread0.242 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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