Low-frequency noise in SiGeC-based pMOSFETs
Bibliographic record
Abstract
The SiGeC ternary alloy seems to be an attractive material system for Si-based device applications, because the incorporation of a small amount of C in the high-mobility SiGe layer offers an additional degree of freedom for tuning the bandgap, band offsets and the lattice strain in group IV heterostructures. In this work, detailed low-frequency noise (LFN) results in SiGeC pMOSFETs are presented. Our experimental results in saturation regime of the SiGe MOSFET show that the noise in SiGeC MOSFETs at gate bias |VGS-VT|<0.4V can be referred to the gate terminal as a noise voltage SVG=VG2, which implies (ΔN) fluctuation with correlated noise in the cap and SiGeC channel currents. Overall, the trend shows that the gate referred noise voltage scales inversely with the gate area, and that the variation of the noise level has log-normal distribution. Therefore, the noise in SiGeC MOSFETs can be expressed as S=Savg*exp(t*σNp), where t=±1,...,±3 is a coefficient selected for desired confidence probability of 0.6,...,0.99 respectively, and σ is the standard deviation of the log-normal distribution of the noise level around its average Savg, later given by (ΔN-Δμ) fluctuation in the cap layer and SiGeC channel of pMOSFET.
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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 | 0.001 |
| 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 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".