High quantum efficiency and low dark count rate in multi-layer superconducting nanowire single-photon detectors
Bibliographic record
Abstract
In this paper, we theoretically show that a multi-layer superconducting nanowire single-photon detector (SNSPD) is capable of approaching characteristics of an ideal SNSPD in terms of the quantum efficiency, dark count, and band-width. A multi-layer structure improves the performance in two ways. First, the potential barrier for thermally activated vortex crossing, which is the major source of dark counts and the reduction of the critical current in SNSPDs is elevated. In a multi-layer SNSPD, a vortex is made of 2D-pancake vortices that form a stack. It will be shown that the stack of pancake vortices effectively experiences a larger potential barrier compared to a vortex in a single-layer SNSPD. This leads to an increase in the experimental critical current as well as significant decrease in the dark count rate. In consequence, an increase in the quantum efficiency for photons of the same energy or an increase in the sensitivity to photons of lower energy is achieved. Second, a multi-layer structure improves the efficiency of single-photon absorption by increasing the effective optical thickness without compromising the single-photon sensitivity.
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 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 | 0.000 |
| 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".