Multi-junction laser power converters exceeding 50% efficiency in the short wavelength infrared
Bibliographic record
Abstract
Photonic or laser power converters are crucial components in power-by-light systems. However, their use in long-distance applications has been hindered by low efficiencies and output voltages within the optical fiber transmission window of 1.3–1.6 μm laser wavelengths. Here, we improve and simplify the design and characterization processes for photonic power converters, exceeding 50% efficiency under 1.446 μm laser light. We develop a calibrated model predicting efficiency gains with increasing bandgap, reaching up to 57% efficiency at a 1.3-μm wavelength. As a first demonstration, we produce a high-efficiency device designed by the model: a four-junction InGaAsP photonic power converter with a conversion efficiency of 53.6% ± 1.3% and an output voltage above 2 V under 15.2 W/cm 2 of 1.446 μm laser light. These advances open new, practical pathways for integrating photonic power converters into telecommunication systems and unlock the potential to further optimize their design with machine learning algorithms trained with our predictive model.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".