High-Performance Multi-Junction C-Band Photonic Power Converters: Calibrated Optoelectronic Model for Next Generation Designs
Bibliographic record
Abstract
Predicting behavior of optoelectronic devices is critical for device design and optimization. Such predictions can be made with a calibrated drift-diffusion model. Recently, photovoltaics using InGaAs as the absorber material, lattice matched to InP, have shown excellent performance in many applications. Further enhancements may be possible by optimizing the designs with an optoelectronic model, calibrated to results from experimental devices. We characterize fabricated InGaAs photonic power converters (PPCs) that have 60 nm, 180 nm, and 540 nm absorber layer thicknesses, using experimental results to calibrate our drift-diffusion model. The calibrated model predicts external quantum efficiencies to better than 1% accuracy and overestimates the open-circuit voltage under 1540 nm illumination by less than 2%. Using this calibrated model, we predict a realistic 1-junction PPC efficiency of up to 46% for a 1540 nm laser, with efficiency limited by carrier collection and series resistance. Improved junction architecture and cell segmentation could help to mitigate both issues. We also fabricated and characterized a 10-junction PPC made of series-connected InGaAs subcells and measured a maximum efficiency of 44% under 1540 nm illumination at 33 W/cm2 power with an open-circuit voltage above 5 V, providing both high efficiency and the voltage required to power electronic circuitry.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".