Temperature Effect on Selectivity of HTSC Josephson Junction Detector
Bibliographic record
Abstract
The most sensitive THz detection is obtained with superconducting Josephson Junction (JJ) detectors, cooled by liquid helium. These detectors require complex cooling systems. JJ detectors implemented in High Temperature Superconductors (HTSC) are considered a viable and more applicable alternative. The frequency of the detected radiation is directly proportional to the voltage of the Shapiro steps. To investigate temperature dependence of the frequency selectivity, i.e., the accuracy of the measured voltage, we implemented JJs detectors in YBa2Cu3O7HTSC thin films, patterned on MgO bicrystal substrate. We improved the detector sensitivity and reduced losses by placing the JJ between the ends of two strips integrated with the antenna, reducing the high mismatch between the impedance of the JJ and that of the Au bow-tie planar antenna. The detector parameters were determined by extensive simulations. High correlation was obtained between the simulations and the experimental results. Error in the measured${{\boldsymbol{V}}_{{\boldsymbol{DC}}}}$, hence in the radiation frequency, was 3.5-7% at 60-70 K, 3-4 orders of magnitude larger than that measured at 40-50 K, less than 0.01%, showing the very strong influence of temperature on the frequency selectivity of high temperature JJs detectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".