MétaCan
Menu
Back to cohort
Record W4327697688 · doi:10.1117/12.2659145

Liquid metal fabrication of ultrathin Ga2O3 and GaN layers for integrated optics

2023· article· en· W4327697688 on OpenAlexfundno aff
Panteha Pedram, Ali Zavabeti, Nitu Syed, Amine Slassi, Chung Kim Nguyen, Benjamin Fornacciari, Anne Lamirand, Jules Galipaud, Arrigo Calzolari, Andreas Boes, Torben Daenke, Sébastien Cueff, Arnan Mitchell, Christelle Monat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
FundersRMIT UniversityEuropean CommissionOntario Ministry of Natural Resources and Forestry
KeywordsMaterials scienceFabricationEllipsometryX-ray photoelectron spectroscopyPhotonicsOptoelectronicsNitrideMetalOxideSemiconductorPlasmaNanotechnologyThin filmChemical engineeringLayer (electronics)Metallurgy

Abstract

fetched live from OpenAlex

We report on the synthesis of 2D GaN materials by the so-called liquid metal chemistry and tuning of their composition between oxide and nitride materials. This technique promises easier integration of 2D materials onto photonic devices compared to traditional “top-down” and “bottom-up” methods. Our fabrication method is carried out via a two-step liquid metal-based printing method followed by a microwave plasma-enhanced nitridation reaction. The synthesis of GaN relies on plasma-treated liquid metal-derived two-dimensional (2D) sheets that were squeeze-transferred onto desired substrates. We characterized the composition and optical properties of the resulting nm-thick GaN films using AFM, XPS, and ellipsometry measurements. Finally, the optical indices measured by ellipsometry are compared with theoretical results obtained by density functional theory (DFT). Our results represent a first step toward integrating 2D materials and semiconductors into electronics and optical devices.

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 score0.020
Threshold uncertainty score0.246

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 head0.022
GPT teacher head0.270
Teacher spread0.247 · 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

Explore more

Same topicGaN-based semiconductor devices and materialsFrench-language works237,207