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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".