A Compact Single-Ended Common-Base Doherty PA in 90-nm BiCMOS With 37.3% Peak PAE for 5G Beamforming Arrays
Bibliographic record
Abstract
This article presents a back-off efficient power amplifier (PA) for mm-wave 5G and upcoming 6G beamforming phased array transceivers (PATs), incorporating advanced circuit designs and novel implementations in both passive and active components. Conventional back-off efficient PAs in the mm-wave frequency range occupy a large chip area, making it hard to fit them into PATs. To overcome this issue, we propose a compact back-off efficient Doherty PA (DPA) with a common base (CB) structure as the core of the PA and small low-loss passive elements. In addition, the proposed architecture moves the role of the input hybrid coupler to the interstage matching network while maintaining DPA functionality. The interstage matching provides the required phases for the main and auxiliary PAs, power division, and impedance matching. The PA prototype is fabricated in the GlobalFoundries 90-nm BiCMOS (9 HP) process. It achieves a peak gain of 20.4 dB at 28.45 GHz with a 1-dB bandwidth of 4.45 GHz. Under large-signal conditions, it archives >19.5-dBm$P_{\mathrm { sat}}$with >36%$\text {PAE}_{\mathrm { sat}}$. Its$P_{1\text {dB}}$at 26, 28, and 30 GHz are 19.4, 19.3, and 19.3 dBm with 38.5%, 37.3%, and 36.8%$\text {PAE}_{1\, \text {dB}}$, respectively. In the 6-dB power back-off region, it reaches efficiencies of 29.1%, 31.1%, and 29.3% at 26, 28, and 30 GHz, respectively. When tested with the NR-FR2 test model at these frequencies, the PA achieves$P_{\mathrm { avg}}$of 8.25, 8.45, and 8 dBm, and$\text {PAE}_{\mathrm { avg}}$of 13.9%, 14.5%, and 13.7% for a 400 M 1-CC 64-QAM signal, maintaining an rms error vector magnitude ($\text {EVM}_{\mathrm { rms}}$) of −25.8, −25.8, and −25.7 dB. In addition, in adjacent channel power ratio (ACPR) tests, the PA achieves −27, −26.2, and −30.8 dBc on the lower side and −28.4, −28.5, and −27.6 dBc on the higher side channels at 26, 28, and 30 GHz, respectively.
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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 | 0.001 | 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".