A 45nm SOI CMOS Class-D mm-Wave PA with &#x003E;10V<inf>pp</inf> differential swing
Bibliographic record
Abstract
The ever-increasing demand for low-cost portable communication devices pushes for higher integration of wireless transceivers in deeply-scaled silicon technologies. Given the overwhelming digital content of a mobile platform, ideally, the RF components should be realized with topologies that allow for their seamless scaling into 22nm and 14nm CMOS technologies. The Power Amplifier (PA) remains one of the most challenging circuit blocks to implement in nanoscale CMOS due to the strict requirements for output power, efficiency and linearity imposed by wireless communication standards. The low breakdown voltage of nanoscale MOSFETs limits the maximum drain voltage swing and the maximum achievable output power. In order to circumvent this problem, a typical approach is to increase the device size and use a reactive matching network to transform the load resistance to a value significantly lower than 50Ω. Nevertheless, due to the typically low-Q passive components that can be manufactured in a nanoscale CMOS process, and because of the high impedance transformation ratio involved, most of the additional output power that would be gained by increasing the device size is wasted in resistive losses in the matching networks, resulting in poor efficiency. This problem is exacerbated at mm-Wave frequencies where the loss of the passive components is even higher, and using lower fT/fMAXthicker oxide or extended drain MOS devices [1] is not viable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".