Optimization of finger spacing for concentrator photovoltaic cells under non-uniform illumination using SPICE
Bibliographic record
Abstract
Concentrator photovoltaic (CPV) technology has come a long way, with multi-junction solar cell efficiencies now reaching up to 44.4%. Front contact grid design, crucial for improving efficiency, is typically performed for uniform illumination, but this does not account for the real world conditions of non-homogeneous irradiance distributions. In this work, we aim to optimize finger spacing for a linear grid under non-uniform illumination by using Simulation Program with Integrated Circuit Emphasis (SPICE) analysis. A two-dimensional distributed resistance model is used to simulate a lattice matched, triple-junction solar cell whose design parameters are determined by curve-fitting current-voltage curves from each sub-cell to a two-diode equivalent-circuit model. Cell efficiency is considered to be a unimodal function that varies with finger spacing so a golden-section search optimization algorithm is used to determine the optimal spacing. Various Gaussian profiles are used to simulate non-uniform illumination and their effects on device performance. Designs based on optimal spacing for non-uniform illumination show an efficiency increase of more than 0.5% absolute at concentrations greater than 500 suns.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".