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Record W1707734365

Analysis and optimasation of quantum cascade structures

2012· article· en· W1707734365 on OpenAlexaboutno aff
Martin Lindskog

Bibliographic record

VenueLund University Publications Student Papers (Lund University) · 2012
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsnot available
FundersStrong
KeywordsCascadeThermalisationPhysicsElectronQuantum chromodynamicsQuantumQuantum cascade laserComputational physicsAtomic physicsQuantum mechanicsChemistry
DOInot available

Abstract

fetched live from OpenAlex

The quantum cascade laser (QCL) is a semiconductor heterostruc- ture using inter sub-band transitions to generate stimulated emission. The quantum cascade detector (QCD) is a similar to the QCL, but the heterostructure is tailored to absorb radiation and give a read-out current. In this work, three planned or realised QCL:s and two QCD:s have been simulated and analysed using a program based on the non- equilibrium Green’s function theory technique (NEGFT) and com- paring to experimental measurements. The importance of electron- electron scattering for thermalisation has been phenomenologically studied by altering the barrier deformation potential and a planned QCL has been optimised to give twice the gain from the original struc- ture. The work has involved corporations with experimental groups at the National Research Council in Ottawa and the University of Wa- terloo, Canada, which resulted in an article published in the Journal of Applied Physics[1]. A new way to display the global behaviour of a QCL in terms of carrier concentration and density of states, by using the spectral func- tion has been developed. For the first time, a QCD has been simulated by NEGFT to give space- and energy-resolved carrier concentrations, density of states and energies of the electronic states. The agreement of NEGFT simulations to experiment is also anal- ysed. The model applies very well to many structures, but the lack of electron-electron interaction causes problems with thermalisation for some structures.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.230
Teacher spread0.221 · 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 designTheoretical or conceptual
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

Citations1
Published2012
Admission routes1
Has abstractyes

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