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Record W4323045624 · doi:10.1002/smsc.202200094

Holes Outperform Electrons in Group IV Semiconductor Materials

2023· article· en· W4323045624 on OpenAlexaff
M. Myronov, J. B. Kycia, P. Waldron, Weihong Jiang, Pedro Barrios, Alex Bogan, P. T. Coleridge, Sergei Studenikin

Bibliographic record

VenueSmall Science · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsNational Research Council CanadaUniversity of Waterloo
Fundersnot available
KeywordsMonocrystalline siliconElectron mobilityGermaniumMaterials scienceWaferSiliconSemiconductorEpitaxyEngineering physicsEffective mass (spring–mass system)ElectronOptoelectronicsCondensed matter physicsAmorphous solidNanotechnologyPhysicsCrystallographyChemistryNuclear physics

Abstract

fetched live from OpenAlex

A record‐high mobility of holes, reaching 4.3 × 106 cm2 V−1 s−1 at 300 mK in an epitaxial strained germanium (s‐Ge) semiconductor, grown on a standard silicon wafer, is reported. This major breakthrough is achieved due to the development of state‐of‐the‐art epitaxial growth technology culminating in superior monocrystalline quality of the s‐Ge material platform with a very low density of background impurities and other imperfections. As a consequence, the hole mobility in s‐Ge appears to be ≈2 times higher than the highest electron mobility in strained silicon. In addition to the record mobility, this material platform reveals a unique combination of properties, which are a very large and tuneable effective g*‐factor (>18), a very low percolation density (5 × 109 cm−2) and a small effective mass (0.054 m 0). This long‐sought combination of parameters in one material system is important for the research and development of low‐temperature electronics with reduced Joule heating and for quantum‐electronics circuits based on spin qubits.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2023
Admission routes1
Has abstractyes

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