<i>In situ</i> control of the resonant frequency of kinetic inductance detectors with multiplexed readout
Bibliographic record
Abstract
Large multiplexing factors are a primary advantage of kinetic inductance detectors (KIDs), but the implementation of high-density arrays still presents significant challenges. Deviations between designed and achieved resonant frequencies are common, and differential loading and responsivity variation across an array may lead to dynamic inter-resonator interactions. It is, therefore, valuable to be able to both set and maintain the resonant frequency of a KID in situ, using the readout system. We show that it is possible to alter the resonant frequency of the devices by more than one resonator linewidth through the application of readout current and establish a new stable operational bias point at the driven frequency by making use of the hysteretic bistability commonly seen as bifurcation in frequency-domain measurements. We examine this interaction using a readout tone at fixed frequency positioned near or within the unbiased resonant bandwidth. Development of a control methodology based on this principle remains in an early stage, but a foundational step is understanding the interaction of the readout current with the resonator, in particular its influence on the resonant frequency. In this work, we study conventional KIDs with no physical isolation from the substrate, so we posit that the readout current primarily interacts with the resonator via non-thermal mechanisms, resulting in a predominantly reactive response. This behavior is reproduced by a simple lumped-element circuit model of the resonance and readout system, providing a straightforward framework for analysis and interpretation. This demonstration is an important early step in the development of techniques that seek to dynamically alter the resonant frequencies of conventional KID arrays and sets the stage for fast active resonant frequency control under operational conditions.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".