Millimeter Wave SOI-CMOS Power Amplifier With Enhanced AM-PM Characteristic
Bibliographic record
Abstract
This paper proposes a novel inter-stage load-pull characterization method to enhance the linearity of millimeter wave integrated power amplifiers (PAs) by minimizing their amplitude-to-phase (AM-PM) distortion without worsening their AM-AM or efficiency performances. The proposed method identifies the optimal solution for the inter-stage matching network which enables the synthesis of a driver stage AM-PM characteristic that is complementary to that of the power stage; consequently, reducing the overall AM-PM distortion of the PA. The proposed technique is applied to design a proof-of-concept 28-31 GHz PA demonstrator using 45 nm silicon-on-insulator CMOS technology. The measurement results obtained under continuous wave excitation at 29 GHz demonstrate an excellent AM-PM characteristic, with phase distortions as low as 0.2° at the 1-dB compression power level of 13.9 dBm and less than 1° at an output power level of up to 16 dBm (very close to the saturation power of 16.6 dBm). This enhanced AM-PM linearity improves the linearizability of the PA. This was confirmed by testing the PA with a 64 quadrature amplitude modulated test signal with an instantaneous bandwidth of 800 MHz. Without applying any digital pre-distortion (DPD) technique, the PA delivers a power added efficiency (PAE) of 8.7% at an average output power (Pavg) of 9.4 dBm while maintaining an error vector magnitude (EVM) of -25 dB. However, after applying a very simple memoryless DPD function with only four coefficients, the PA can operate at a higher Pavg of 11.1 dBm with a much better PAE of 12.2% while still maintaining an acceptable EVM of -25.2 dB. Thanks to the proposed technique, the PAE of the proposed PA can be improved by 40% with a very simple application of a low cost and low complexity DPD technique.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".