A 2.85-mm<sup>2</sup> Wideband RF Transceiver in 40-nm CMOS for IoT Micro-Hub Applications
Bibliographic record
Abstract
This paper presents a 2.85-mm2 0.4-6 GHz RF transceiver in 40-nm CMOS for low-cost and low-power IoT micro-hub applications. A single-path receiver (RX), an all-digital phase-locked loop (ADPLL), and a digital transmitter (DTX) (including a digital power amplifier, DPA) are integrated. In the RX, to reduce the chip area and power consumption, an inductor-less capacitive-feedforward wideband LNA and a Gm-C filter-based dc offset cancellation (Gm-C-DCOC) technique are proposed. The RX achieves a noise figure (NF) of 1.3-4.9 dB over 0.4-6 GHz while consuming 31 mW. The measured average IIP3, in-band P1dB, and calibrated IIP2 of the receiver are 4.5 dBm, −13.8 dBm, and 67.5 dBm, respectively. In the ADPLL, a calibration-free retiming fractional frequency dividing (FFD) scheme based on a parasitic insensitive digital phase interpolator is adopted, for releasing the narrow loop bandwidth limitation and achieving better phase noise without active noise cancellation techniques. In the DTX design, a piecewise bias voltage (PBV) technique is proposed for the AM-AM linearization. It achieves a peak output power of 22.5 dBm at 840 MHz with a drain efficiency of 60.2%. The DPA can work in a high-power mode without PBV and a middle-power mode with PBV for modulation with different complexity. Thanks to PBV, the DTX achieves 5.9%, 5.3% EVM for 2MS/s 16-QAM and 64-QAM without digital pre-distortion (DPD), respectively. The designed broadband reconfigurable transceiver can support most of the common IoT protocols in most key metrics.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".