Effects of body biasing on the low-frequency noise of NMOSFETs from a 130-nm CMOS technology
Bibliographic record
Abstract
We investigate the impact of body biasing on the low frequency noise (LFN) performances of NMOS transistors from a transistors 130 nm CMOS technology. The body-to-source voltage VBS was varied from - 0.5 to + 0.5 V for reverse and forward mode substrate biasing. A detailed electrical characterization was performed and the benefits of the body bias analysed in terms of current and maximum transconductance variations. Noise measurements were first performed at low drain bias VDS = 25 mV and VBS = 0 V in order to discuss the noise model. Results are in agreement with the carrier number fluctuation theory. Bulk bias dependence of the LFN was investigated at VDS = VDD = 1.2 V. Significant noise reduction is observed in the subthreshold regime when applying a forward body bias. In strong inversion, the noise level is found to be approximately independent of the substrate bias VBS.
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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) | 0.001 | 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".