An autotransformer impedance transformation technique for efficient power harvesting of passive transponders and wireless microsensors
Bibliographic record
Abstract
A novel autotransformer impedance transformation technique to improve the power efficiency of RF-to-DC power harvesting of passive transponders and wireless microsensors is proposed. The method utilizes a step-up autotransformer with a large turn ratio inserted between the antenna and the voltage multiplier to boost the signal prior to rectification while providing a matching impedance to the antenna. To have a large voltage gain while minimizing the silicon consumption of the step-up autotransformer, the number of turns of the spiral of the primary winding is kept low and the width of the spiral of the secondary winding is made smaller than that of the primary winding. This also effectively minimizes the effect of spiralsubstrate parasitic capacitances of the transformers, eliminating the drawback of the widely used impedance transformation with resonating LC networks. Implemented in IBM-130 nm 1.2V CMOS technology, simulation results have demonstrated that the proposed step-up autotransformer impedance transformation technique significantly improve the power efficiency.
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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 | 0.000 | 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".