Lowest surface recombination velocity on n-type crystalline silicon using PECVD a-Si:H/SiN x bi-layer passivation
Bibliographic record
Abstract
Energy conversion efficiency of crystalline silicon (c-Si) solar cells manufactured on thin substrates is strongly influenced by the recombination losses of photo-generated charge carriers at the surface and in its proximity. Intrinsic hydrogenated amorphous silicon (i-a-Si:H) deposited using DC saddle-field plasma enhanced chemical vapour deposition (PECVD) at a low temperature of ~200°C reduces recombination losses of photo-generated carriers through passivation of defects at the surface. This study reports on high quality surface passivation achieved using a dual layer approach wherein a 70nm amorphous silicon nitride (SiNx) capping layer is deposited on a less than 10nm thin i-a-Si:H layer. While the a-Si:H layer is effective in passivating the interface recombination sites, SiNx is deemed to incorporate field-effect passivation, thus providing a minority carrier mirror. Additionally, SiNx layer acts as an anti-reflection coating with a low absorption coefficient in the optical frequency range of interest. The SiNx deposition conditions, known to strongly influence the passivating quality of the dual layer structure, were systematically investigated using the response surface methodology (RSM). The optimal deposition parameters obtained from the RSM study were experimentally verified to yield the lowest surface recombination velocity of 3.5 cm/s on 1-2 Ω-cm n-type FZ c-Si using a PECVD a-Si:H/SiNx bi-layer passivation stack.
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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".