An ultra low noise telecom wavelength free running single photon detector using negative feedback avalanche diode
Bibliographic record
Abstract
It is challenging to implement genuine free running single-photon detectors for the 1550 nm wavelength range with simultaneously high detection efficiency (DE), low dark noise, and good time resolution. We report a novel read out system for the signals from a negative feedback avalanche diode (NFAD) [M. A. Itzler, X. Jiang, B. Nyman, and K. Slomkowski, "Quantum sensing and nanophotonic devices VI," Proc. SPIE 7222, 72221K (2009); X. Jiang, M. A. Itzler, K. ODonnell, M. Entwistle, and K. Slomkowski, "Advanced photon counting techniques V," Proc. SPIE 8033, 80330K (2011); M. A. Itzler, X. Jiang, B. M. Onat, and K. Slomkowski, "Quantum sensing and nanophotonic devices VII," Proc. SPIE 7608, 760829 (2010)], which allows useful operation of these devices at a temperature of 193 K and results in very low darkcounts (∼100 counts per second (CPS)), good time jitter (∼30 ps), and good DE (∼10%). We characterized two NFADs with a time-correlation method using photons generated from weak coherent pulses and photon pairs produced by spontaneous parametric down conversion. The inferred detector efficiencies for both types of photon sources agree with each other. The best noise equivalent power of the device is estimated to be 8.1 × 10(-18) W Hz(-1/2), more than 10 times better than typical InP/InGaAs single photon avalanche diodes (SPADs) show in free running mode. The afterpulsing probability was found to be less than 0.1% per ns at the optimized operating point. In addition, we studied the performance of an entanglement-based quantum key distribution (QKD) using these detectors and develop a model for the quantum bit error rate that incorporates the afterpulsing coefficients. We verified experimentally that using these NFADs it is feasible to implement QKD over 400 km of telecom fiber. Our NFAD photon detector system is very simple, and is well suited for single-photon applications where ultra-low noise and free-running operation is required, and some afterpulsing can be tolerated.
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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 | 0.001 |
| 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".