Time-mode techniques for fast-locking phase-locked loops
Bibliographic record
Abstract
A fast-locking phase-locked loop (PLL) with variable loop dynamics is proposed. The PLL employs a time amplifier (TA) with a variable gain to amplify the phase difference between the reference clock and the output of the voltage controlled oscillator (VCO). It operates by dynamically increasing the bandwidth of the PLL during locking state to speed up locking process and decreasing the bandwidth of the PLL in locked state to optimize the performance of the PLL. The insertion of the TA also reduces the time constant of the loop filter without sacrificing performance, thereby allowing the reduction of the resistance and capacitance of the loop filter subsequently their silicon and power consumption. The increased width of Up and Down pulses also enable the reduction of the current of the charge pump while achieving the same variation of the control voltage thereby lowering the power consumption. Two identical PLLs, one with the TA and the other without were designed in an IBM 0.13 μm CMOS 1.2 V technology and analyzed using SpectreRF from Cadence Design Systems with BSIM4 device models. Both critically damped and under-damped cases were investigated. Simulation results demonstrate that in the critically damped case, the lock time of the PLL with the TA is 0.42 μs while that without is 0.52 μs. In the under damped case, the lock time of the PLL with the TA is 0.30 μs while that without is 1.54 μs.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".