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Record W3027300319 · doi:10.1109/tasc.2020.2996151

Resonant Frequency Adjusting of an MSA via Laser Ablation of the Input Coil

2020· article· en· W3027300319 on OpenAlexafffund
Clifford E. Plesha, J. B. Kycia

Bibliographic record

VenueIEEE Transactions on Applied Superconductivity · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsMaterials scienceElectromagnetic coilAmplifierAblationLaserLaser ablationPrinted circuit boardOptoelectronicsInterference (communication)MicrostripOpticsChannel (broadcasting)Electrical engineeringPhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A convenient method to permanently adjust the operating frequency of a microstrip superconducting quantum interference device amplifier (MSA) was investigated. An optical laser cutter was used to ablate the input coil of an MSA, bonded to a printed circuit board (PCB), to increase the MSA's operational frequency. During the ablation process, the MSA did not have to be electrically or physically decoupled from the PCB. Using atomic force microscopy imaging, a cutting parameter of a single laser pulse of 1.8 mJ was found to ablate the 50-nm-thick input coil and 10 nm of the SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> substrate beneath the input coil. After ablation, the bias current was unchanged and the gain of the MSA was relatively consistent, indicating that the ablation process does not affect the Josephson junctions. The resonant frequency of the input was adjusted from 180 to 270 MHz.

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 score0.147
Threshold uncertainty score0.844

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 communication0.0000.001
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 head0.023
GPT teacher head0.224
Teacher spread0.202 · 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
Published2020
Admission routes2
Has abstractyes

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