Characterization and modeling of high-frequency noise in MOSFETs for RF IC design
Bibliographic record
Abstract
This paper presents a thorough description of the high-frequency noise characterization and modeling of CMOS transistors for radio frequency (RF) integrated circuit (IC) design. It covers two main topics: high-frequency noise characterization and physics-based noise models. In the first section, two de-embedding procedures are presented for noise and scattering parameter de-embedding to get rid of the parasitic effects from the probe pads and interconnections in the device-under-test (DUT). With the intrinsic noise parameters, two extraction methods to obtain the channel noise, induced gate noise and their correlation in MOSFETs are discussed and experimental results are presented. Based on the noise information obtained in the first section, the second part of the paper presents physics-based noise models for the noise sources of interest in deep submicron MOSFETs. It discusses the model derivation, channel noise enhancement in deep submicron MOSFETs and impact of channel length modulation (CLM) effect. Finally a simple and accurate analytical model for channel noise calculation will be presented.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".