Hydrogen passivation effects on spatially resolved charge trap densities in Si(100)–SiO2
Bibliographic record
Abstract
As silicon-based devices continue to shrink to the nanoscale, traps at the Si–SiO2 interface pose increasing challenges to device performance. These traps reduce channel carrier mobility and shift threshold voltages in integrated circuits and introduce charge noise in quantum systems, reducing their coherence times. Knowledge of the precise location of such traps aids in understanding their influence on device performance. In this work, we demonstrate that frequency-modulated atomic force microscopy allows the detection of individual traps. We use this to study how sample preparation, specifically the introduction of a buried hydrogen termination layer and post-processing annealing in forming gas (N2 + H2), affects the density of donor-like traps in Si(100)–SiO2 systems. We spatially map and quantify traps in both conventionally prepared (“pristine”) silicon samples and those processed under ultrahigh vacuum for hydrogen resist lithography (HRL). We confirm previous studies demonstrating hydrogen passivation of traps and find that hydrogen termination further reduces the donor-like trap density. We also observe a significant reduction in two-level donor-like traps in the hydrogen-terminated samples compared to pristine silicon samples. These findings suggest that HRL-prepared silicon may offer advantages for high-performance nanoscale and atomic-scale devices due to reduced trap densities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".