A Highly-Efficient RF Energy Harvester Using Passively-Produced Adaptive Threshold Voltage Compensation
Bibliographic record
Abstract
This article presents a highly-efficient radio frequency energy harvester that utilizes an extra matching network to produce a passively-amplified adaptive compensation voltage. The compensation voltage produced on the gate of the transistors reduces the transistors’ conduction loss by increasing the gate-source voltage when transistors are on and reduces the leakage current by producing a negative gate-source voltage when the transistors are off. This is the first work that produces an adaptive compensation voltage without using active components, resulting in a significantly higher conversion efficiency if passive components of high quality are utilized. The mathematical derivations show that the forward conduction loss and the leakage current of the transistors are minimized by utilizing the proposed technique, increasing the overall efficiency. The proposed rectifier is fabricated in a TSMC 130 nm standard CMOS process, and measurement results and simulation results are in good agreement. Measurement results show that the rectifier achieves the maximum efficiency of 61% and 63.4% for battery load of 1.2 V and 1.5 V, respectively, which is at least 20 % larger than the efficiency of the conventional Dickson’s rectifiers.
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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 | 0.000 |
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".