Nonlinear Modeling and Harmonic Recycling of Millimeter-Wave Rectifier Circuit
Bibliographic record
Abstract
This paper presents and demonstrates a harmonic harvesting technique, which aims at rerectifying and recycling the rectifier output harmonics in order to increase the RF-to-dc conversion efficiency. Firstly, an analytical framework is developed based on the Ritz-Galerkin technique to investigate the output power distribution of a simple millimeter-wave rectifier circuit over its dc and generated harmonics components. The numerical results show that about 32% and 66% of the output power is distributed over the dc component and the first harmonic, respectively. In order to recycle and harvest the first harmonic power component, a 35-GHz voltage doubler rectifier implemented in microstrip technology and capable of harmonic harvesting is then studied and designed. To evaluate the performance of the proposed rectifier, the conventional voltage doubler and the harmonic signal rectifiers are fabricated and measured. The measured RF-to-dc conversion efficiencies of 34% at 20-mW input RF power for the harmonic harvester configuration, and 23% at the same input RF power level for the conventional voltage doubler are observed. Moreover, it is shown that the proposed harmonic rectifier suggests about 12% efficiency improvement compared to previously reported millimeter-wave rectifiers at the same level of input power (20 mW). The proposed rectifier configuration can find potential applications in the development of millimeter-wave wireless power transmission devices operating at medium power range (1-100 mW).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".