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Record W4283330300 · doi:10.1063/5.0103165

Aluminum air bridges for superconducting quantum devices realized using a single-step electron-beam lithography process

2022· article· en· W4283330300 on OpenAlexafffund
Michal Kononenko, Adrian Lupaşcu

Bibliographic record

VenueApplied Physics Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationCMC Microsystems
KeywordsMicrowaveResonatorElectron-beam lithographyCoplanar waveguideSuperconductivityMaterials scienceJosephson effectLithographyElectronYield (engineering)Cathode rayOptoelectronicsPhysicsResistCondensed matter physicsNanotechnologyQuantum mechanics

Abstract

fetched live from OpenAlex

In superconducting quantum devices, air bridges enable increased circuit complexity and density, and mitigate the risk of microwave loss arising from mode mixing. We implement aluminum air bridges using a simple process based on single-step electron-beam gradient exposure. The resulting bridges have sizes ranging from 20 µm to 100 µm, with a yield exceeding 99% for lengths up to 36 µm. When used to connect ground planes in coplanar waveguide resonators, the induced loss contributed to the system is negligible, corresponding to a loss per bridge less than 1.0×10−8. The bridge process is compatible with Josephson junctions and allows for the simultaneous creation of low loss bandages between superconducting layers.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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