Measuring the device‐level EQE of multi‐junction photonic power converters
Bibliographic record
Abstract
Abstract Multi‐junction photonic power converters (PPCs) are photovoltaic cells used in photonic power transmission systems that convert monochromatic light to electricity at enhanced output voltages. The junctions of a multi‐junction PPC have overlapping spectral responsivity, which poses a unique challenge for spectrally resolved external quantum efficiency (EQE) measurements. In this work, we present a novel EQE measurement technique based on a wavelength‐tunable laser system and characterize the differential multi‐junction device‐level EQE (dEQEMJ) as a function of the monochromatic irradiance over seven orders of magnitude. The irradiance‐dependent measurements reveal three distinct irradiance regimes with different dEQEMJ. For the experimentally studied 2‐junction GaAs‐based device, at medium irradiance with photocurrent densities between 0.3 and 90 mA/cm2, dEQEMJ is independent of irradiance and follows the expected EQE of the current‐limiting subcell across all wavelengths. At higher irradiance, nonlinear device response is observed and attributed to luminescent coupling between the subcells. At lower irradiances, namely, in the range of conventional EQE measurement systems, nonlinear effects appear, which mimic luminescent coupling behavior but are instead attributed to finite shunt resistance artifacts that artificially inflate dEQEMJ. The results demonstrate the importance of measuring the device‐level dEQEMJ in the relevant irradiance regime. We propose that device‐level measurements in the finite shunt artifact regime at low monochromatic irradiance should be avoided.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".