An RF-to-DC Rectifier With High Efficiency Over Wide Input Power Range for RF Energy Harvesting Applications
Bibliographic record
Abstract
This paper presents, a wide input range, 4-stage threshold voltage compensated RF-to-DC power converter, designed to efficiently convert RF signals to dc voltages by applying an optimum compensation voltage produced by subthreshold auxiliary transistors. The proposed optimally compensated rectifiers can achieve higher efficiency over a wider input power range compared to other threshold voltage compensation circuits where the level of the compensation is limited by the circuit structure and varies with input power. The designed rectifier is implemented in three possible ways. This proposed compensation technique can be applied to a rectifier chain with a relatively low number of stages. Designed and implemented in a 130 nm CMOS technology, the proposed rectifier exhibits a measured PCE of above 20% over the 8.5-dB input power range while driving a 1-MQ load resistor at 896-MHz. For the same load and utilizing a minimal number of compensated rectifier stages, the proposed circuit exhibits a maximum PCE of 43% at -11 dBm for single-ended Dickson-based CMOS rectifiers. The proposed circuit demonstrates a -20.5 dBm sensitivity for 1 V output across a 1-MQ resistive load.
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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".