Optical Simulation of Multijunction Solar Cells Based on III-V Nanowires on Silicon
Bibliographic record
Abstract
Arrays of III-V direct-bandgap semiconductor nanowires are promising candidates for future photovoltaic devices due to their high optical absorption and their ability to be grown on low cost semiconductor substrates like silicon. The core-shell structure is particularly interesting as the electron-hole pair separation occurs in the radial direction and the photogenerated minority carriers have to travel short distances (the radius of the nanowires) thus improving the collection probability in case of well passivated nanowire surfaces. The aim of this study is to find the optimal geometry (length, height and diameter) of a GaAs nanowire array grown on a silicon substrate in order to have the best absorption of the incident photons. For this purpose, we have performed electromagnetic simulations with a homemade Rigorous Coupled Wave Analysis (RCWA) software. Our simulations take into account the core-shell structure, the passivation layer (GaAlAs) and the anti-reflection coating, but also the necessity to achieve current matching between the GaAs nanowire-based and the silicon substrate solar cells. This requirement is justified by the fact that the final goal is to process a tandem solar cell with junctions connected in series.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".