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Record W4388497107 · doi:10.21203/rs.3.rs-3558020/v1

Development of Microwave Kinetic Inductance Detectors for a THz On-Chip Spectrometer

2023· preprint· en· W4388497107 on OpenAlexaff
P. S. Barry, Rong Nie, E. Shirokoff, J. P. Filippini, Jake Connors, Marcin Gradziel, Dale Mercado, Locke D. Spencer, Serena Tramm, N. Trappe, M. Zemcov

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsUniversity of Lethbridge
FundersScience Mission DirectorateNuclear Safety and Security CommissionDivision of Materials ResearchNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsSpectrometerDetectorTerahertz radiationPhysicsChipMicrowaveWaveguideOpticsCoplanar waveguideKinetic inductanceAntenna (radio)OptoelectronicsInductanceElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

Abstract Far-infrared (FIR) wavelength light provides a wealth of information about astrophysical processes. Large field-of-view FIR spectrometers face a variety of challenges that can be bypassed by utilizing on-chip filter bank technology. To this end, we have been developing microwave kinetic inductance detectors (MKIDs) to couple to on-chip spectrometric technologies. We utilize a unique two-silicon wafer design to create a waveguide for an on-chip spectrometer between 1.4 and 2 THz. The spectrometer's principle of operation is electromagnetic radiation traveling down a center waveguide before being picked off by a resonance cavity and absorbed by a detector. The MKIDs are designed to couple directly to the waveguide using an annular antenna shape for the inductor. Alignment of the two-wafer design is achieved with two dowel pins and a hole and slot pattern in our custom designed box. We present the design and early test data from a 18-channel prototype device. The prototype yielded the full 18 resonances with quality factors Q c of about 5 ∗ 10 4 and Q i of about 1 ∗ 10 4 . We plan to use the technology we developed to create an n × n pixel device targeting R = 100.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.135
GPT teacher head0.388
Teacher spread0.253 · 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 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

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