Laser annealing heals radiation damage in avalanche photodiodes
Bibliographic record
Abstract
Avalanche photodiodes (APDs) are a practical option for space-based quantum communications requiring single-photon detection. However, radiation damage to APDs significantly increases their dark count rates and thus reduces their useful lifetimes in orbit. We show that high-power laser annealing of irradiated APDs of three different models (Excelitas C30902SH, Excelitas SLiK, and Laser Components SAP500S2) heals the radiation damage and several APDs are restored to typical pre-radiation dark count rates. Of nine samples we test, six APDs were thermally annealed in a previous experiment as another solution to mitigate the radiation damage. Laser annealing reduces the dark count rates further in all samples with the maximum dark count rate reduction factor varying between 5.3 and 758 when operating at ${-}80^{\circ}\mathrm{C}$ . This indicates that laser annealing is a more effective method than thermal annealing. The illumination power to reach these reduction factors ranges from 0.8 to 1.6 W. Other photon detection characteristics, such as photon detection efficiency, timing jitter, and afterpulsing probability, fluctuate but the overall performance of quantum communications should be largely unaffected by these variations. These results herald a promising method to extend the lifetime of a quantum satellite equipped with APDs.
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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 | 0.000 | 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".