Silicon Photovoltaics Using Conducting Photonic Crystal Back‐Reflectors
Bibliographic record
Abstract
Abstract Currently, research is being directed towards thinning conventional 200–300µm thick silicon photovoltaic cells by an order of magnitude or more. The benefits of reducing the cell thickness include decreased material costs, enhanced cell flexibility, and reduced effects of light‐induced degradation. However, one of the major challenges associated with reducing the active region to this extent is the corresponding reduction of light absorption. To mitigate this effect it has been proposed that the cell should incorporate enhanced light‐trapping strategies. One potential approach to enhance light trapping in thin photovoltaic cells is to structure the back‐reflector in the form of a photonic crystal (PC). It has recently been shown that two fundamental attributes of PC back‐reflectors optically coupled to thin semiconductor films contribute to enhanced absorption in the semiconductor: (i) the PC back‐reflector behaves as a perfect mirror, exhibiting complete reflection over stop‐gap frequencies; and (ii) the PC–semiconductor film interface couples incident light into resonant states that propagate along the plane of the film, thereby further enhancing the absorption. Although the ability of PC back‐reflectors to enhance absorption is encouraging, significant challenges arise when attempting to incorporate this light trapping technique in photovoltaic devices. Herein, we describe the underlying physical mechanisms that give rise to absorption enhancements in thin Si wafers featuring PC back‐reflectors, and describe hurdles that will have to be surmounted in order to reduce‐to‐practice a PC back‐reflector into an actual PV device.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".