Characterisation of a Low-Noise Tuneable Silicon Single-Photon Avalanche Diode
Bibliographic record
Abstract
Silicon single-photon avalanche diodes (Si-SPADs) are widely used in lightwave applications such as quantum communication, medical imaging, time-of-flight systems and quantum optical metrology, due to their sensitivity, accuracy, compactness and cost-effectiveness. The most important performance metric depends on its intended application. Each metric has a dependence on operating parameters such as bias voltage and operating temperature. Some metrics, e.g. detection efficiency and timing precision, exhibit performance trade-offs with noise. However, plug-and-play Si-SPADs typically have fixed bias voltage and operating temperature, which significantly limits user control, adaptability, and performance. In this study, a recently developed commercially available free-space Si-SPAD with adjustable bias voltage, temperature control, an integrated frequency counter and a large sensing area was evaluated. Key performance metrics were traceably measured as functions of bias voltage and temperature, including dark count rate, dead time, timing precision, afterpulse probability and photon detection efficiency at 852 nm. Results are benchmarked against existing reports of other commercially available Si-SPAD detectors. Notable strengths include low dark count rates (4 to 10s-1), a large sensing area (diameter = 500 μm), and high timing precision (163 to 212ps), whilst maintaining a detection efficiency greater than 40%. These characteristics make the detector a strong candidate for deployment across a range of applications, particularly those operating outside of lab conditions where operational flexibility is essential.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".