A Fully Integrated Distributed Active Transformer Power Amplifier with Injection Locking
Bibliographic record
Abstract
This work explores the design of a novel integrated RF power amplifier in IBM's 0.13 micron RF CMOS process.Through a combination of architectures, the series output transformer coupling of a distributed active transformer (DAT) topology is paired with a gain enhancing injection-locking architecture in an attempt to realize a combination of high output power, gain and efficiency; while minimizing die area and supply voltage.The DAT topology simultaneously provides high quality impedance transformation and power combining in the output network that can be used to overcome the low breakdown and high knee voltages -and lossy on-chip passive components -inherent to silicon processes.While improving the overall maximum output power, the large transistors of the DAT require large amplitude driving signals which limit the gain of the system.The injection-locking technique can be used to reduce the input drive voltage for an amplifier circuit, thereby increasing the gain and, consequently, the power-added efficiency.By employing this novel hybrid architecture, the proposed injection-locking DAT power amplifier achieves a P1dB of 25.94 dBm with a maximum gain of 14.5 dB and peak PAE of 22% from a 1.5 V dc supply voltage (in simulation).The measurement results show discrepancies from the simulated results provided, and several hypotheses as to the cause of these differences are explored. GND GND
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".