Trap Recovery by in-Situ Annealing in Fully-Depleted MOSFET With Active Silicide Resistor
Bibliographic record
Abstract
This work reports first original results on the impact of active in-situ electro-thermal recovery, on the electrical and low-frequency noise characteristics of N-type MOS transistor with thick high-k metal gate oxide, from 28 nm Fully Depleted Silicon-On-Insulator (FDSOI) process. In order to recover “typical” device characteristics, four cycles of local thermal annealing up to 590K are applied for 14 ms each, using an active silicide source. Experimental results reveal an important improvement of the “corner” transistor’s I-V behavior allowing the recovery of “typical” device characteristics. An increase of the maximum transconductance by 43% is obtained. In the same time, a typical device stays unaffected by this local annealing. Low-frequency noisemeasurements showa clear reduction of the 1/f noise and Random Telegraph Noise by almost one decade, after the electro-thermal recovery. This can explain the improvement of the electrical characteristics by annealing of defects.
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".