Protocols for Healing Radiation-Damaged Single-Photon Detectors Suitable for Space Environment
Bibliographic record
Abstract
Photon detectors based on single-photon avalanche photodiodes (SPADs) are well-suited for satellite-based quantum communication because of their advantageous operating characteristics as well as their relatively straightforward and robust integration into satellite payloads. However, space-borne SPADs will encounter damage from space radiation, which usually manifests itself in the form of elevated dark counts. Methods for mitigating this radiation damage have been previously explored, such as thermal and optical (laser) annealing. Here we investigate in a laboratory, using a CubeSat payload, laser annealing protocols in terms of annealing laser power and annealing duration, for their possible later use in orbit. Four Si SPADs (Excelitas SLiK) irradiated to an equivalent of 10 years in low Earth orbit (LEO) exhibit very high dark count rates (DCRs) (>300 kcps at −22 °C operating temperature) and significant saturation effects. We show that annealing them with optical power between 1 and 2 W yields reduction in DCR by a factor of up to 48, as well as regaining SPAD sensitivity to a very faint optical signal (on the order of single photon) and alleviation of saturation effects. Our results suggest that an annealing duration as short as 10 s can reduce dark counts, which can be beneficial for power-limited small-satellite quantum communication missions. Overall, annealing power appears to be more critical than annealing duration and number of annealing exposures.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".