Efficient Dual-band Ultra-Low-Power RF Energy Harvesting Front-End for Wearable Devices
Bibliographic record
Abstract
This paper presents a novel dual-band ultra-low power RF energy harvesting front-end for wearable devices, wireless sensor networks, and the Internet of things (IoT) designed in standard 130 nm CMOS technology. The proposed dual-band RF energy harvester operates at ISM bands of 915 MHz and 1.85 GHz and uses an efficient summation network to combine power from different frequency bands. A dual-band antenna receives signals from two different bands which are boosted by two different matching networks. Then, two self-compensated cross-coupled rectifiers convert the RF signals provided by the two different bands into DC output voltages. At the output of the two rectifiers, a summation network using switches and a control circuit combines power from different bands, charging the output capacitor. The post-layout simulation results demonstrate a sensitivity of -33 dBm for 1 V output at a capacitive load and the peak end-to-end efficiency is 43.2% at -18 dBm when two frequency bands are available. When only 915 MHz or 1.85 GHz is available, the results demonstrate a high peak efficiency of 42.3% and 44% at -16 dBm and -17 dBm, 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) | 0.002 | 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".