MétaCan
Menu
Back to cohort
Record W4285398469 · doi:10.1149/ma2022-01201106mtgabs

(Invited) Photoluminescence Monitored Digital Photocorrosion of GaAs/AlGaAs Nanoheterostructures

2022· article· en· W4285398469 on OpenAlexaff
Jan J. Dubowski, Jonathan Vermette, René St‐Onge

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsEtching (microfabrication)MonolayerOptoelectronicsReactive-ion etchingMaterials scienceNanotechnologySemiconductorPhotoluminescenceLayer (electronics)

Abstract

fetched live from OpenAlex

Conventional etching techniques do not have the ability to achieve precision of monolayer etching of crystalline solid materials. In recent years, atomic layer etching (ALE) has emerged as a method addressing this challenge based on the application of self-limiting sequential reactions. Nevertheless, monitoring of the ALE process, important for real time information about the location of the etching front, is not trivial and could be an expensive task. We have investigated the innovative process of digital photocorrosion (DIP) of GaAs/AlGaAs nanoheterostructures, which is based on photoexcitation driven decomposition of these materials.1 The process is carried out in a flow cell filled with etchants designed for selective removal of the photocorrosion products. For instance, water dilutes relatively easily As and most of the Ga oxides, but dilution of Ga2O3 or Al oxides and hydroxides requires dedicated etchants. Under optimized conditions, DIP allows etching of (001) GaAs/AlGaAs nanoheterostructures with average rates approaching 1 Å/cycle. The process could be monitored in situ by measuring, e.g., open circuit potential2 or photoluminescence3 effect. The sensitivity of these effects to the surface states of a semiconductor allows for convenient marking of the location of a GaAs/AlGaAs interface when crossed by the etching front. In this presentation, I will discuss mechanisms of DIP of GaAs/AlGaAs nanoheterostructures and the conditions for formation of stoichiometric surfaces. Examples of the DIP process applied for formation of an unusual density alkanethiol self-assembled monolayers and operation of optical devices for detecting electrically charged molecules immobilized in the vicinity of a semiconductor-electrolyte interface will also be discussed. Finally, I will also address the potential of this economically attractive invention for the research of other semiconductor materials. __________________ 1. S. Aithal, N. Liu and J. J. Dubowski, Journal of Physics D: Applied Physics 50 (3), 035106 (2017). 2. S. Aithal and J. J. Dubowski, Appl Phys Lett 112 (15), 153102 (2018). 3. M. R. Aziziyan, H. Sharma and J. J. Dubowski, Acs Appl Mater Inter 11 (19), 17968-17978 (2019).

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 score0.067
Threshold uncertainty score0.701

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)0.0000.000

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 head0.008
GPT teacher head0.202
Teacher spread0.195 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicNanowire Synthesis and ApplicationsFrench-language works237,207