Subcell‐Resolved EQE Method Using Reverse Voltage Biasing for Multijunction Photovoltaics With Overlapping Subcell Absorptance
Bibliographic record
Abstract
ABSTRACT External quantum efficiency (EQE) measurements of individual subcells in multijunction photovoltaic devices are essential to evaluate current matching and to iterate the design process. The standard light biasing technique used to measure subcell EQE falls short when multiple subcells absorb within the same spectral region. In this work, we demonstrate a three‐step reverse voltage biasing EQE method, which measures any number of subcells with overlapping absorptance: (1) A light bias is applied to generate current mismatch between the subcells. (2) Current–voltage (I–V) characteristics are measured into reverse bias, where the limiting subcell enters reverse‐bias breakdown and the device current climbs to a plateau at the photocurrent of the next limiting subcell, producing a staircase I–V curve. (3) Each subcell EQE curve is measured using a voltage bias within its current plateau. We demonstrate this approach for a two‐junction GaAs‐based photonic power converter, comparing to the standard light biasing method and revealing better than 0.8% absolute agreement when the top junction is preferentially biased in the reverse voltage biasing method. We demonstrate the viability of the method by measuring the EQE of all subcells in a six‐junction GaAs‐based photonic power converter.
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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 | 0.001 | 0.001 |
| 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".