Investigation of Aluminum-Alloyed Local Contacts for Rear Surface-Passivated Silicon Solar Cells
Bibliographic record
Abstract
We present a comprehensive study on the rear contact formation of rear surface-passivated silicon solar cells by full-area screen printing and alloying of aluminum pastes on the locally opened passivation layer. We show that the point contact distance has a significant influence on the local alloying process for the contact formation resulting in different structural and electrical contact properties when applying conventional Al pastes. Increasing the distance leads to 1) high contact depths resulting in an enlargement of the contact area and 2) severely reduced thicknesses of the Al-doped p+regions in the contact points, leading to a strong increase in recombination within the contact points. This inadequate contact formation can be directly linked to the deficiently low percentage of silicon that dissolves into the Al-Si melt during alloying. We demonstrate that by intentionally adding Si to the Al paste, the contact point geometry can be significantly improved and particularly becomes independent of the contact distance. Further investigations on the internal reflectance and the specific resistivity of the rear contact suggest an upper limit for the Si content added to the Al paste. In summary, we present a simple way to significantly improve the rear contact formation of rear surface-passivated silicon solar cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".