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Record W4283073547 · doi:10.1002/9781119579182.ch1

Photoconductivity: Fundamental Concepts

2022· other· en· W4283073547 on OpenAlexaff
Safa Kasap

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPhotoconductivityPhotocurrentOptoelectronicsNoise (video)Materials scienceSemiconductorBand gapDopingFlicker noiseFlickerPhysicsCondensed matter physicsElectrical engineeringComputer scienceNoise figure

Abstract

fetched live from OpenAlex

Photoconductivity is defined, and the importance of electrical contacts are highlighted by examining the origin of the dark current flowing through a photoconductor. The Shockley–Ramo theorem is explained and the photoconductive gain and the necessary prerequisites for its manifestation are discussed. Major recombination kinetics are addressed, including the Shockley–Read–Hall statistics, Simmons–Taylor formulation, and Langevin recombination, and their main features are highlighted. Photoconductivity experiments have been extensively used by numerous researchers to characterize various semiconductor materials. Principles of steady-state and modulated photoconductivity (frequency-resolved photoconductivity) are introduced along with their main features in extracting material characteristics such as the density of states in the bandgap. The effects of traps on transient photoconductivity and modulated photoconductivity are discussed in simple terms and extended to include the Taylor–Simmons formulation. The phase and the amplitude of the modulated photocurrent are explained in relation to the density of states in the bandgap. Major noise sources in a photoconductor are described and the importance of generation–recombination and flicker noise are highlighted. Particular attention is given to noise in a-Si:H films and its dependence on doping.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.137
Threshold uncertainty score1.000

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)0.1370.000

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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations14
Published2022
Admission routes1
Has abstractyes

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