Novel Cascaded Ring Oscillator Vernier based TDC Architecture with a Feedthrough Reference Oscillator
Bibliographic record
Abstract
Time to digital converters (TDCs) are widely used in particle physics and medical imaging to determine the relative arrival time of photons and other particles. Vernier TDCs are one of the most attractive options due to their power consumption efficiency and their small footprint. We present our newly patented innovative architecture of cascaded-stage Vernier TDCs comprised of a feedthrough reference oscillator. This architecture lead to successive Vernier measurements that result in a lower number of cycles for the same dynamic range to resolution ratio (DRRR), leading to reduced conversion time and lower jitter. To validate this architecture, a test chip was designed in 0.18~μm TSMC technology, integrating four types of cascaded Vernier TDCs with an increasing number of stages. The performance of the chip was assessed using a modular test platform specifically designed for TDCs. For comparison purposes, all TDCs were tested with a LSB of 50~ps and a dynamic range of 20~ns. The experimental results demonstrate the highest gain in performance between 1 stage Vernier and 2 stage Vernier TDCs with the precision improved from 36~ps~rms to 21~ps~rms, the maximum number of turns from 110 to 25 counts and the dead time, from 266.4~ns to 76.5~ns, on average. Adding Vernier stages is only relevant for a higher DRRR. Therefore, the 3-stage and 4-stage Vernier TDCs also show performance gains, but improvements are limited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".