A 50nm high-k poly silicon gate stack with a buried SiGe channel
Bibliographic record
Abstract
This report presents high-k poly ring oscillators with a performance of τ=16.2 ps/st at 6E-6A/st for Lg=80 nm. This was achieved by using a SiGe channel for nFET and pFET. A minimum device length of 50 nm was built by using this technique. The transistors reach Ion=120μA/μm at Ioff=20 pA/mum with Tinv=2.4 nm, resulting in a normalized delay of 8.7 ps (Vdd=1.0 V). This is the best high-k poly pFET performance published so far (~Ion=220μA/μm at Vdd=-1.2V). We demonstrate the combination of the SiGe channel with common performance enhancement techniques like stress liners and rotated channel as used in the hybrid oriented substrate technique. The buried channel improves short channel effects and has no reliability trade off. Although hole mobility is enhanced in SiGe channel transistors, further gain was observed in narrow width structures. Peak mobility can be up to 130 cm2/Vs for pFET, extending the universal mobility for silicon. After investigating Vt and short channel effects we are able to show, that half of the gain is caused by more efficient stress in narrow width structures. The other half is attributed to EPI loading effects in small structures. Fluorine I/I in the well improves NBTI behavior by more than one decade in time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".