Using TFM Analysis and Memory Map Calibration for Designing Linear and Monotonic LC DCOs
Bibliographic record
Abstract
This papers shows a proof-of-concept and experimental design on how to design a linear and monotonic LC DCO using a combination of Topographical Field Map (TFM) analysis (or TFMA) and a one time Programmable Memory Map (PMM) calibration (or PMMC) scheme. Linearity and monotonicity is important in (all-digital) phase-locked loops (AD-PLLs) since it helps predict the locking mechanism and system analysis better - leading to more robust timers and synchronizers; which can further be used in the implementation and improvement of better health care equipment, smart devices, and cloud hardware. For improving the linearity and monotonicity of the DCO in this paper, TFMA is first used to design the DCO in its linear region, then PMMC is used to make the DCO monotonic. The calibration process is only done once to map DCO linearity. Instead of direct access, an external stimuli is given to the memory map, which then controls the DCO. The measured DCO using the proof-of-concept calibration system had a linearity R2of 0.997, a DNL of 0.65 LSB over its entire control-bit spectrum, and a DNL of 0.11 LSB over its fine tuning spectrum.
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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 | 0.001 | 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) | 0.002 | 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".