Physical Model of Noise Mechanisms in SOI and Bulk-Silicon MOSFETs for RF Applications
Bibliographic record
Abstract
The noise mechanisms at high frequencies in MOSFETs are analyzed and an analytical model is presented for devices operating at gigahertz frequencies. The proposed model is applied to floating body silicon-on-insulator (SOI) as well as bulk-silicon MOS transistors and experimentally verified. The model accounts for the mechanisms of 1) channel thermal noise; 2) shot-noise due to impact ionization; and 3) substrate back-gate-coupled thermal noise. Compact, closed-form analytical expressions of the noise power spectral density and the minimum noise figure (NF) are presented. At the same technology level, the experimental data and the model show that SOI MOSFETs are able to attain lower NF than bulk-silicon devices by reduction of the back-gate transconductance. However, the higher drain electric field in the SOI, and the parasitic bipolar action and floating body enhance impact-ionization-associated shot-noise, which becomes the limiting noise mechanism at drain voltages higher than the drain onset voltage of ldquokinkrdquo effect. A correlation between the onset voltage and the DC electrical characteristics is shown.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".