Light management in ultra-thin photonic power converters for 1310 nm laser illumination
Bibliographic record
Abstract
We designed and optimized ultra-thin single junction InAlGaAs photonic power converters (PPC) with integrated back reflectors (BR) for operation at the telecommunications wavelength of 1310 nm and numerically studied the light trapping capability of three BR types: planar, cubic nano-textured, and pyramidal nano-textured. The PPC and BR geometries were optimized to absorb a fixed percentage of the incident light at the target wavelength by coupling finite difference time-domain (FDTD) calculations with a particle swarm optimization. With 90% absorptance, opto-electrical simulations revealed that ultra-thin PPCs with 5.6- to 8.4-fold thinner absorber layers can have open circuit voltages (Voc) that are 9-12% larger and power conversion efficiencies (PCE) that are 9-10% (relative) larger than conventional thick PPCs. Compared to a thick PPC with 98% absorptance, these ultra-thin designs reduce the absorber layer thickness by 9.5-14.2 times while improving theVocby 12-14% and resulting in a relative PCE enhancement of 3-4%. Of the studied BR designs, pyramidal BRs exhibit the highest performance for ultra-thin designs, reaching an efficiency of 43.2% with 90% absorptance, demonstrating the superior light trapping capability relative to planar and cubic nano-textured BRs.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".