Vertical Multi-Junction Laser Power Converters with 61% Efficiency at 30 W Output Power and with Tolerance to Beam Non-Uniformity, Partial Illumination, and Beam Displacement
Bibliographic record
Abstract
Stable and reliable optical power converting devices are obtained using vertical multi-junction laser power converters. They are based on the GaAs and the InP material systems and are used for power-over-fiber or power-beaming applications. This study demonstrates that, in addition to providing the overall best conversion efficiencies with output voltages ideal for various applications, these semiconductor photovoltaic devices are very tolerant to beam non-uniformity, partial illumination, or beam displacement variations. Examples are given with two tight beams, each covering as little as ~7% of the cell area. An optical input power of 10 W was converted with still an efficiency of Eff ~59.4%. For an input power of 20 W, the illuminated area was set to ~22% without significantly affecting the conversion efficiency of Eff ~60%. Remarkably, for a beam diameter at ~65% of the chip length (i.e., covering ~35% of the chip area), a converted power of 29.5 W was obtained using a 12-junction GaAs device with a conversion efficiency of 61%. For a 10 junction InP-based device, an efficiency of Eff = 51.1% was obtained at an output voltage reaching as high as Voc = 5.954 V for an average optical intensity of 69 W/cm2 and an illumination area of ~57%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".