Multi-Terminal GaInP/GaInAs/Ge Solar Cells for Subcells Characterization
Bibliographic record
Abstract
Improvement of triple-junction (3J) III-V/Ge solar cells efficiency is hindered by the low current produced by the top and middle cells relative to the bottom cell (Ge). This can be explained by the difficulty of characterizing, on an individual basis, the subcells. We investigate the fabrication process of multi-terminal multi-junction solar cells (MTMJSC) and its potential as a promising architecture to independently characterize subcells of multi-junction solar cells. Here, we study monolithic triple-junction solar cells, with an InGaP top cell, an InGaAs middle cell and a Ge bottom cell interconnected by tunnel junctions. We demonstrate a fabrication process for MTMJSC on commercial wafers for characterization applications purposes. I-V measurements, under illumination, of two-terminals and MTMJSC were compared to validate that the MTMJSC fabrication process does not degrade the cells’ performance. The dark current of each subcell was also measured and an ideal-diode model used to determine the subcells electrical parameters. The results suggest a method to measure the relative absorption and the opto-electrical couplings between the subcells unambiguously, through EQE and electroluminescence measurements, based on basic micro-fabrication processes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".