High-Efficiency Threshold-Voltage-Compensated RF Energy Harvester With Gate and Body Biasing Techniques
Bibliographic record
Abstract
This article proposes a high-efficiency radio frequency energy harvester (RFEH) design that implements gate and body biasing techniques to decrease the threshold voltage of the transistors in their conduction phases and increase it in their reverse-biasing phases. The proposed scheme simultaneously reduces conduction and leakage losses to achieve higher power conversion efficiency (PCE) for the RFEH. The biasing voltages at the gate and body terminals are generated by amplifying the input signal using passive components, which avoids additional power consumption. To verify the efficacy of the proposed technique, the RFEH system is designed and fabricated using TSMC’s 130 nm standard complementary metal–oxide–semiconductor (CMOS) process for two input power levels: −20 and −10 dBm. The design process of the proposed topology is provided to find the optimum values of passive components of the biasing circuits and the matching network. The measured PCE of the proposed gate-body-biased RFEH systems is 42.9% and 57.9% at input power levels of −20 and −10 dBm, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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 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".