Kinetic Inductive Model of a Millimeter-Wave High-Temperature Superconducting Optoelectronic Mixer
Bibliographic record
Abstract
We introduce and analyze an optoelectronic mixer (OEM) based on the kinetic inductive photoresponse in high-temperature superconducting (HTS) films. This device combines photodetection and optoelectronic mixing functions through a nonlinear change in the kinetic inductance of the HTS film when it is irradiated by an optically modulated microwave signal. A comprehensive theoretical analysis is presented using the two-temperature model to describe the nonbolometric (quantum) photoresponse and the kinetic inductance model for the electrical part. Upon the optical irradiation, the change in the electron temperature of the HTS film leads to a parametric change in the kinetic inductance of the photoexcited HTS bridge, which in the presence of a bias current produces a periodic voltage waveform. In order to obtain the temporal behavior and the frequency content of the output voltage in terms of the input local oscillator and modulation frequencies, the kinetic inductance model and Fourier series analysis have been used and their physical consequences have been discussed in detail. The merit characteristics of the kinetic inductive HTS-OEM, such as intrinsic and optical conversion gains and noise temperature, are evaluated and compared with other high-frequency mixers. This is followed by the numerical simulation of the proposed device.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".