An efficient CMOS RF power extraction circuit for long-range passive RFID tags
Bibliographic record
Abstract
Effective matching and efficient power conversion play key roles in long- range power telemetry. This thesis discusses challenges and suggests solutions for long-range power telemetry with an emphasis on radio-frequency identification (RFID) applications. As a proof-of-concept a radio-frequency (RF) power harvesting system in a 0.13-µm CMOS technology is designed, fabricated, and successfully tested. The RF power harvesting system must maintain matching over the the wide operation frequency range of passive RFID tags, mandated by EPC- global. In this work, we first analyze the series-inductor matching network and show that there is a trade-o between bandwidth and efficiency. We then derive some guidelines for matching circuit design for RFID tags. To solve the matching problem over a wide frequency range, an adaptive matching system is proposed. At the startup, this system turns on while the rest of the chip is still inactive, and automatically tunes the matching network to achieve its maximum output voltage. Then the rest of the chip wakes up and functions as normal. A new CMOS rectifier stage is also proposed. This stage is capable of efficient operation even with very low input powers. In addition, this rectifier stage can be cascaded to reach higher output voltages without significantly compromising the overall efficiency. Combination of low-power performance and cascadability makes this rectifier suitable for long-range RFID tags. The test setup and measurement results are also discussed in a separate chapter. The measurement results show a 50% rectifiers efficiency at 4-µW input power. To the best of our knowledge, to date, this is the highest efficiency reported for rectifiers operating at such a low input power. Also, as compared to the output voltage at the nominal center frequency of the input matching network, the system shows less than 6% drop in output voltage over the entire 55-MHz bandwidth of the system which verifies the effectiveness of adaptive matching.
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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) | 0.003 | 0.002 |
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".