Rear side dielectrics on interdigitating <i>p</i><sup>+</sup>-(<i>i</i>)-<i>n</i><sup>+</sup> back-contact solar cells − hydrogenation vs. charge effects
Bibliographic record
Abstract
Polysilicon-on-oxide (POLO) passivating contacts and interdigitated back-contact (IBC) cell technologies have recently attracted a lot of interest as candidates for the implementation in the next generation of solar cells. An IBC cell with POLO junctions for both polarities − a POLO2-IBC cell − has to electrically isolate the highly defective p+ and n+ poly-Si regions on the rear side of the cell to avoid parasitic recombination. Inserting an initially undoped, intrinsic (i) region between the p+ and n+ poly-Si regions was demonstrated to successfully prevent the parasitic recombination in the transition region of ISFH's 26.1%-efficient POLO2-IBC cell. In order to further improve the conversion efficiency towards 27%, we apply hydrogen-donating dielectric layer stacks to the p+-(i)-n+ POLO interdigitating rear side to enhance the passivation quality of the POLO junctions. We indeed show a significant improvement of POLO junctions on symmetrical full-area homogenously doped reference samples, but when we apply a hydrogen-donating layer stack on the p+-(i)-n+ POLO interdigitating rear side, we observe a strong degradation in the performance of the POLO2-IBC cell. We attribute this to the formation of a conductive channel between the p+ and n+ poly-Si regions due to the strong negative charge density of the hydrogen-donating layer 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".