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Record W3165160693 · doi:10.48550/arxiv.2105.10114

Ferroelectricity in Polar ScAlN/GaN Epitaxial Semiconductor Heterostructures

2021· preprint· en· W3165160693 on OpenAlexaff
Joseph Casamento, Ved Gund, Hyunjea Lee, Kazuki Nomoto, Takuya Maeda, Benyamin Davaji, Mohammad Javad Asadi, John Wright, Yu‐Tsun Shao, David A. Muller, Amit Lal, Xing Xing, Debdeep Jena

Bibliographic record

VenuearXiv (Cornell University) · 2021
Typepreprint
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsGrace (Canada)
Fundersnot available
KeywordsMaterials scienceFerroelectricityHeterojunctionOptoelectronicsEpitaxySemiconductorMolecular beam epitaxyCoercivityCondensed matter physicsNanotechnologyDielectric

Abstract

fetched live from OpenAlex

Room temperature ferroelectricity is observed in lattice-matched ~18% ScAlN/GaN heterostructures grown by molecular beam epitaxy on single-crystal GaN substrates. The epitaxial films have smooth surface morphologies and high crystallinity. Pulsed current-voltage measurements confirm stable and repeatable polarization switching in such ferroelectric/semiconductor structures at several measurement conditions, and in multiple samples. The measured coercive field values are Ec~0.7 MV/cm at room temperature, with remnant polarization Pr~10 μC/cm2 for ~100 nm thick ScAlN layers. These values are substantially lower than comparable ScAlN control layers deposited by sputtering. Importantly, the coercive field of MBE ScAlN is smaller than the critical breakdown field of GaN, offering the potential for low voltage ferroelectric switching. The low coercive field ferroelectricity of ScAlN on GaN heralds the possibility of new forms of electronic and photonic devices with epitaxially integrated ferroelectric/semiconductor heterostructures that take advantage of the GaN electronic and photonic semiconductor platform, where the underlying semiconductors themselves exhibit spontaneous and piezoelectric polarization.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.037
GPT teacher head0.163
Teacher spread0.126 · 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 designSimulation or modeling
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

Citations4
Published2021
Admission routes1
Has abstractyes

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