Integration of Laser‐Patterned Photonic Crystals in Si Solar Cells
Bibliographic record
Abstract
Abstract Photonic crystal (PhC) light trapping is predicted to enhance absorption beyond the Lambertian limit, potentially increasing silicon solar cell efficiencies above 28%. However, integrating PhC structures into high‐efficiency devices at scale remains challenging. PhC textures are integrated into back‐contacted silicon solar cells by combining femtosecond laser ablation of alumina masks with dry etching. Excellent surface passivation is maintained using an isotropic defect‐removal process based on ammonia peroxide mixture (APM). This preserves the front‐side texture and keeps optical reflection low. The PhC‐patterned cells deliver minority carrier lifetimes and carrier collection efficiencies comparable to state‐of‐the‐art high efficiency devices. A certified efficiency of 23.1% is achieved. The quantum efficiency of the thick (190–290 µm) solar cells, however, shows no clear wave‐optical resonances under standard conditions, despite the high structural and electronic quality. Scalability is improved by applying direct laser writing with Gaussian and Bessel beams and developing a periodically anchored mask design. This enables uniform, large‐area patterning. These advancements mark a key step toward the practical implementation of PhC‐enhanced silicon photovoltaics.
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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".