A versatile photon counting system with active afterpulse suppression for free-running negative-feedback avalanche diodes
Bibliographic record
Abstract
InGaAs/InP-based negative-feedback avalanche diodes (NFADs) have been demonstrated to be an excellent option for photon detection at telecom wavelengths in quantum communication applications, where photon arrival times are random. However, it is well-known that the operation of NFADs at low temperatures (193 K or below) is crucial to minimize the effects of afterpulsing and high dark count rates (DCRs). In this work, we present a new versatile readout electronics system with active afterpulse suppression that also offers flexible cooling options. Through the characterization of two NFAD detectors from Princeton Lightwave, Inc. and a thorough evaluation of our electronics' performance under various operating conditions, we demonstrate the effectiveness of this readout system in improving the performance of NFAD-based photon detectors. At the optimal bias for NFADs, our electronics were able to significantly reduce the afterpulsing probability by a factor of 200 for dead times ranging from 5 to 20 µs following each detection event. This helps to keep the total DCRs at around 100 counts per second or less for a 20 µs hold-off time. The versatility of our detection system makes NFADs a cost-effective alternative to more complex detectors, such as superconducting nanowire single-photon detectors, in the research of long-distance quantum communications and low-noise single photon detectors at telecommunication wavelengths.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| 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".