Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a through description of radio frequency (RF) noise characterization and modeling of CMOS transistors. It begins with the definition of the four noise parameter of a two-port network - minimum noise figure (NF min ), equivalent noise resistance (R n ), optimized source impedance (R opt ) and optimized source reactance (X opt ). These four parameters are used in device characterization and it is shown how they can be calculated by using the noise two-port network theory and a circuit simulator. Then two de-embedding procedures are discussed in detail 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). Ideally there is no frequency and geometry limitation for the method based on a cascade configuration. Methods to directly extract the channel noise, induced gate noise and their correlation from the RF and noise measurements are developed and the extracted noise sources as a function of frequency and bias condition for different channel lengths a presented. Some design consideration for the design of low noise circuits - how to select the device size, choice of DC bias conditions and design device layout, are presented. Finally, some published noise models for the channel noise, induced gate noise and their correlation are discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it