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Record W2565392340 · doi:10.1088/1361-6463/50/3/035106

Photocorrosion metrology of photoluminescence emitting GaAs/AlGaAs heterostructures

2016· article· en· W2565392340 on OpenAlexafffund
Srivatsa Aithal, Neng Liu, Jan J. Dubowski

Bibliographic record

VenueJournal of Physics D Applied Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCMC Microsystems
KeywordsPhotoluminescenceHeterojunctionMaterials scienceMetrologyOptoelectronicsNanotechnologyOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract High sensitivity of the photoluminescence (PL) effect to surface states and chemical reactions on surfaces of PL emitting semiconductors has been attractive in monitoring photo-induced microstructuring of such materials. To address the etching at nano-scale removal rates, we have investigated mechanisms of photocorrosion of GaAs/Al 0.35 Ga 0.65 As heterostructures immersed either in deionized water or aqueous solution of NH 4 OH and excited with above-bandgap radiation. The difference in photocorrosion rates of GaAs and Al 0.35 Ga 0.65 As appeared weakly dependent on the bandgap energy of these materials, and the intensity of an integrated PL signal from GaAs quantum wells or a buried GaAs epitaxial layer was found dominated by the surface states and chemical reactivity of heterostructure surfaces revealed during the photocorrosion process. Under optimized photocorrosion conditions, the method allowed resolving a 1 nm thick GaAs sandwiched between Al 0.35 Ga 0.65 As layers. We demonstrate that this approach can be used as an inexpensive, and simple room temperature tool for post-growth diagnostics of interface locations in PL emitting quantum wells and other nano-heterostructures.

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 score0.092
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.260
Teacher spread0.250 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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