Electronic Passivation of Crystalline Silicon Surfaces Using Spatial‐Atomic‐Layer‐Deposited HfO<sub>2</sub> Films and HfO<sub>2</sub>/SiN<sub><i>x</i></sub> Stacks
Bibliographic record
Abstract
Spatial atomic layer deposition (SALD) is applied to the electronic passivation of moderately doped (≈1016 cm−3) p‐type crystalline silicon surfaces by thin layers of hafnium oxide (HfO2). For 10 nm thick HfO2 layers annealed at 400 °C, an effective surface recombination velocity Seff of 4 cm s−1 is achieved, which is below what has been reported before on moderately doped p‐type silicon. The one‐sun implied open‐circuit voltage amounts to iVoc = 727 mV. After firing at 700 °C peak temperature in a conveyor‐belt furnace, as applied in the production of solar cells, still a good level of surface passivation with an Seff of 21 cm s−1 is attained. Reducing the HfO2 thickness to 1 nm, the passivation virtually vanishes after firing (i.e., Seff > 1000 cm s−1). However, by adding a capping layer of plasma‐enhanced‐chemical‐vapor‐deposited hydrogen‐rich silicon nitride (SiNx) onto the 1 nm HfO2, a substantially improved firing stability is attained, as demonstrated by Seff values as low as 30 cm s−1 after firing, which is attributed to the hydrogenation of interface states. The presented study demonstrates that SALD‐deposited HfO2 layers and HfO2/SiNx stacks have the potential to evolve into an attractive surface passivation scheme for future 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) | 0.000 | 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".