Systematic Co-Design of Matching Networks and Rectifiers for CMOS Radio Frequency Energy Harvesters
Bibliographic record
Abstract
This paper presents a systematic methodology for the co-design of matching network and rectifier of radio frequency (RF) harvesters that results in maximum power conversion efficiency (PCE) for a given available power. This method is based on our newly developed rectifier model capable of calculating the CMOS Dickson's rectifier's input/output voltages at a given input power developed for low/high input power regimes. The proposed model allows for the co-design of the matching network and the rectifier in a fraction of time that takes for the design of the RF energy harvester using previously developed models relying on the knowledge of rectifier's input voltage levels where a computationally extensive iterative design procedure must be performed because of the interdependence of the rectifier's input voltage, the input power, and the matching network's and rectifier's parameters. The proposed methodology is capable of accurately predicting matching network components' sizes for both the lossless and lossy matching networks for a maximum power transfer. Utilizing the proposed methodology, the designers can produce efficiency contour plots for a given input power for finding the optimum matching network and rectifier's parameters for maximum PCE. The model, simulation, and measurement results for different parameters and input power levels in a 130-nm process are in good agreement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".