Design of High-Order Type-II Delay-Locked Loops With a Fast-Settling-Zero-Overshoot Step Response and Large Jitter-Rejection Capabilities
Bibliographic record
Abstract
In this paper, a design method for high-order delay-lock loops (DLLs) is presented and verified through simulations and physical experiments. The general approach is based on selecting the closed-loop transfer function of the DLL, together with identifying the coefficients of the phase-detector and voltage-controlled delay line, and subsequently, solving for parameters of the loop filter of the DLL. Past DLL design approaches relied more on establishing a desired phase margin requirement than attempting to establish a desired input-output behavior. This limited the realization of DLLs to second order; largely a result of the complicated mathematics that arise. As the method proposed in this paper is based on selecting a desired closed-loop transfer function, the issues of stability or phase margin never come to the forefront. This paper will show how a DLL can be designed to achieve a fast-settling-zero-overshoot step response with large jitter-rejection capabilities. The method is simple and easy to execute. No optimization or iteration is necessary. The method is similar to the methods used to design active-RC filter circuits. A fully programmable experimental prototype involving a custom IC implemented in a 130-nm IBM CMOS process was constructed. DLLs with orders ranging from second to eighth will be investigated.
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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 | 0.000 |
| 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".