Nonharmonic Injection-Locked Phase-Locked Loops With Applications in Remote Frequency Calibration of Passive Wireless Transponders
Bibliographic record
Abstract
This paper proposes a low-power remote frequency calibration method for passive UHF wireless transponders. The frequency of the local oscillator of passive UHF wireless transponders is adjusted 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 proposed. The power consumption of the proposed IL-PLL is minimized by operating it the subthreshold. A detailed analysis of the nonharmonic 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 IL-PLL has been fabricated in TSMC 0.18- μm 1.8-V six-metal 1-poly CMOS technology. The performance of the IL-PLL is validated using both simulation and measurement results. The measured power consumption of the IL-PLL with a 10-mV (640-pW) 1-MHz injection signal is 960 nW. The lock range of the IL-PLL is 30 kHz without integrating feedback and 400 kHz with integrating feedback. The frequency of the locked oscillator drifts over time at a rate of 5 Hz/ms when the external injection signal is removed.
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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 | 0.001 |
| 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".