Radio-Frequency Power Harvest and Remote Clock Frequency Calibration of Passive Wireless Microsystems
Bibliographic record
Abstract
This thesis deals with radio frequency power harvest and remote calibration of system clock of passive wireless microsystems. The proposed method of RF power harvesting utilizes a step-up transformer inserted between the antenna and voltage multiplier of passive wireless microsystems to perform both impedance transformation for power matching and voltage amplification prior to rectification. The series resistance of the primary winding is minimized while in the secondary winding, the shunt capacitive losses are minimized. The detailed analysis of the proposed method and simulation results from Spectre of Cadence Design Systems are presented. The proposed power-matching and gain -boosting network, together with voltage multipliers, has been implemented in TSMC-0.18..m 1.8V6-meatl CMOS technology with thick metal options. For the purpose of comparison, a LC power-matching and gain-boosting network with the identical voltage multiplier has also been implemented on the same chip. Measurement results demonstrate that the proposed transformer power-matching and gain-boosting technique greatly improves the power sensitivity and efficiency as compared with widely used LC matching approaches. The proposed calibration method adjusts the frequency of the local oscillator of passive UHF wireless transponders to the desired values using an injection-locked phase-locked loop (IL-PLL). A new relaxation oscillator whose oscillation frequency is less sensitive to supply voltage fluctuation is also proposed. The power consumption of the proposed IL-PLL is minimized by operating it the sub-threshold. A detailed analysis of non-harmonic injection locking of relaxation oscillators including locking and pulling dynamics is presented. A new integrating feedback is proposed to increase the lock range and hold the locked frequency in the absence of the injection signal. The proposed ILL-PLL has been fabricated in TSMC-0.18.
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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) | 0.001 | 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".