400-million-pixel superconducting delay-line camera with 30-ps readout circuit
Bibliographic record
Abstract
We propose using a current-biased kinetic inductance detector (CB-KID) to image the distribution of hot spots induced by a localized external stimulus on orthogonal XY superconducting meander lines. We utilize a delay-line technique to trace the propagation of internal signals for a pair of signals arising from each hot spot. We use the timestamps of signal arrivals at the electrodes to determine the position of each hot spot (x, y). Because the signal velocity inside the detector is ultrafast at ∼20% the speed of light, a readout circuit with a temporal resolution faster than 250 ps is necessary to resolve the position of a hot spot with a precision of 1.5 μm, which is the size of a meander pitch. In our earlier work, we developed a 1-ns CB-KID readout circuit, but it was not fast enough to resolve each segment as a unit tick step of the hot-spot position. In this work, we developed a new Kalliope-II readout circuit equipped with a front-end main circuit for a continuous readout data acquisition system (AMANEQ) and a high-resolution time-to-digital converter that works at a temporal resolution of 30 ps. This allows us to resolve images with smaller pixels down to 1.5 × 1.5 μm2. Clearly, the CB-KID sensor can detect various stimuli, such as photons and neutrons. As a superconducting detector, the CB-KID realized a 100 000 000-pixel camera with a 15 × 15 mm2 sensitive area. Occasionally, the size of the hot spot in the CB-KID is larger than the meander pitch, enabling one to determine the center-of-gravity position of the hot spots in units of half pitch (0.75 μm). This could enable a superconducting camera with up to 400 000 000 submicrometer pixels over a 15 × 15 mm2 area. This development could open up great possibilities when CB-KID is applied to photonic measurements.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".