Firing stability of tube furnace‐annealed n‐type poly‐Si on oxide junctions
Bibliographic record
Abstract
Abstract Stability of the passivation quality of poly‐Si on oxide junctions against the conventional mainstream high‐temperature screen‐print firing processes is highly desirable and also expected since the poly‐Si on oxide preparation occurs at higher temperatures and for longer durations than firing. We measure recombination current densities (J0) and interface state densities (Dit) of symmetrical samples with n‐type poly‐Si contacts before and after firing. Samples without a capping dielectric layer show a significant deterioration of the passivation quality during firing. The Dit values are (3 ± 0.2) × 1011 and (8 ± 2) × 1011 eV/cm2 when fired at 620°C and 900°C, respectively. The activation energy in an Arrhenius fit of Dit versus the firing temperature is 0.30 ± 0.03 eV. This indicates that thermally induced desorption of hydrogen from SiH bonds at the poly‐Si/SiOx interface is not the root cause of depassivation. Postfiring annealing at 425°C can improve the passivation again. Samples with SiNx capping layers show an increase in J0 up to about 100 fA/cm2 by firing, which can be attributed to blistering and is not reversed by annealing at 425°C. On the other hand, blistering does not occur in poly‐Si samples capped with AlOx layers or AlOx/SiNy stacks, and J0 values of 2–5 fA/cm2 can be achieved after firing. Those findings suggest that a combination of two effects might be the root cause of the increase in J0 and Dit: thermal stress at the SiOz interface during firing and blistering. Blistering is presumed to occur when the hydrogen concentration in the capping layers exceeds a certain level.
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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".