Effective Hydrogenation of Poly-Si Passivating Contacts by Atomic-Layer-Deposited Nickel Oxide
Bibliographic record
Abstract
In recent years, passivating contacts based on SiO2/poly-Si have proven to be an enabling technology for Si solar cells. Effective hydrogenation of the interfacial SiO2is vital for realizing efficient contacts. Hydrogen-rich dielectrics, such as SiNxand Al2O3, are commonly employed for hydrogenation, whereas also recently,n-type conductive oxides, such as In2O3:Sn and ZnO, have been demonstrated to yield excellent hydrogenation. This study presents the use of ap-type metal oxide, specifically NiO, as a suitable hydrogenation source. Thep-type character of NiO makes it an interesting candidate for hydrogenation because of its potential use in selective contacting structures. Herein, we show that NiO, synthesized by atomic layer deposition (ALD), can be used to hydrogenate poly-Si/SiO2contacts effectively. Furthermore, we benchmark its hydrogenation performance to the established ALD ZnO/Al2O3stack and provide insights into the hydrogenation process. On planar surfaces, NiO yields almost as excellent results as ZnO/Al2O3stacks, whereas it lags behind on more challenging textured surfaces. Interestingly, even though elastic recoil detection analysis reveals that ALD NiO is rich in hydrogen, secondary ion mass spectrometry measurements show that, when NiO is compared to the ZnO/Al2O3stack, less hydrogen is present at the Si/SiO2interface after annealing. This is explained from effusion measurements, which show substantial effusion of hydrogen from NiO around 300 °C. Hence, Al2O3capping is further employed to prevent hydrogen loss and on textured wafers, the NiO/Al2O3stacks on poly-Si achieve an implied open-circuit voltage of 728 mV, confirming the excellent hydrogenation from ALD metal oxides.
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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) | 0.002 | 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".