Nanometer-Mesa Inverted-Pyramid Photonic Crystals for Thin Silicon Solar Cells
Bibliographic record
Abstract
The usage of ultrathin flexible silicon foil can further extend the functionality of silicon and emerging silicon-based tandem solar cells particularly in building and vehicle-integrated photovoltaics where high-efficiency, lightweight, and flexible solar panels are highly desired. However, silicon’s relatively weak optical absorption coefficient especially in the near infrared (NIR) region limits its optoelectronic applications with a reduced wafer thickness. Herein, we seek to overcome this limitation by exploring the wave interference phenomenon for effective absorption of NIR light in ultrathin silicon. Particularly, inverted pyramid photonic crystals (PhCs) with nano–micrometer-scale feature sizes are carved directly on silicon. Detailed experimental and theoretical studies are presented by systematically examining the optical properties of PhC-integrated thin silicon substrates (down to a 10 μm thickness). The corresponding maximum photocurrent density for a thin absorber is projected and compared with that predicted by Lambertian’s limit. In contrast to traditionally configured microscale inverse pyramids, we show that a small mesa width is critical to achieving high optical performance for a wave-interference-based absorption enhancement. Mesa widths as small as 35 nm are realized over a large wafer-scale fabrication using facile techniques. The optical performance of 10 μm silicon indicates that an ideal photocurrent density approaching 40 mA/cm 2 is feasible. This study indicates that photonic crystals provide strong wave interference in ultrathin silicon, and in particular, we observe high optical absorption even after removing more than 90% of the silicon from conventional “thick” Si wafers.
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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 | 0.001 |
| 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".