Light Trapping Characteristics of Photonic Crystal Constructs and Randomly Textured Thin Silicon
Bibliographic record
Abstract
We present an experimental study investigating the light trapping properties of ultra-thin silicon with inverted pyramidic photonic crystals of nano-scale mesas, as well as random pyramidal textured silicon. Using conventional industry compatible technologies - photolithography and wet alkaline etching - photonic crystals are fabricated with uniformity, homogeneity, high reproducibility wherein the mesas are of nanoscale widths (i.e., below 40 nm). For thin-silicon foils of 20 and 40 µm thicknesses, both structures demonstrate significant enhancement in absorption compared to flat thin silicon, especially after the addition of double-layer anti-reflection coating and a metallic back reflector. For photonic crystals, the enhancement is due to the significant increase in optical path length especially at long wavelengths where effects such as parallel to interface refraction are observed. For random pyramids, the wide range of pyramid sizes obtained here (from 500 nm to 5 µm) show a broadband absorption enhancement where small pyramids suppress the reflection at short wavelengths and large pyramids contribute to trapping the long-wavelength photons. The maximal photocurrent densities for these structures are comparable to that for the Lambertian surface and exceed 40 mA/cm2 for silicon foil thickness down to 20 µm,
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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".