A Non-Iterative Method for Design of Radio Frequency Energy Harvesters
Bibliographic record
Abstract
This paper proposes a non-iterative method for the design of Radio Frequency Energy Harvesters (RFEHs) with maximum power conversion efficiency (PCE) at any given input power level. Because of the non-linear interdependency of the rectifier’s input impedance and its input voltage to matching network’s and rectifier’s parameters, the design of an RFEH with maximum efficiency requires numerous lengthy transient simulations of the entire energy harvester. Splitting the design space into two separate spaces which only interact with each other through the input voltage of the rectifier, the design goal can now be redefined to finding an optimum input voltage amplitude that maximizes the efficiency of the rectifier while enabling maximum power transfer from antenna to the input of the rectifier at the same time. Using the proposed method, the number of the required simulations to find optimum design values is significantly reduced compared to all previous methods reported in the literature, which also has been experimentally verified by designing three battery-loaded RFEHs at different input power levels in TSMC’s 130nm CMOS process. To further accelerate the design process, closed-form equations to calculate the efficiency and the input resistance of the rectifier are derived for the battery-loaded Dickson charge pump rectifiers.
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 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) | 0.000 | 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".