Recent Developments and Design Challenges of High-Performance Ring Oscillator CMOS Time-to-Digital Converters
Bibliographic record
Abstract
Time-to-digital converters (TDCs) are increasingly used as building blocks in biomedical imaging, digital communication, and measurement instrumentation systems. When fabricated in deep-submicrometer (DSM) CMOS technology, TDCs have outstanding time stamping capability on the order of picoseconds. Typically, the timing resolution of a TDC directly determines the minimum resolvable spatial resolution in time-of-flight (ToF) measurements. It also limits the signal-to-noise ratio in ToF positron emission tomography and the in-band noise in an all-digital phase-locked-loop. In TDCs, good linearity and precision result in high measurement accuracy, while the detectable range is limited by its dynamic range. In addition, size and power consumption are of significant importance in large-scale array implementations such as image sensors. Here, we discuss the most recent developments in CMOS TDCs, with an emphasis on ring-oscillator-based TDC and its variants, due to their suitability for array designs with less area overhead. In addition, key performance metrics, and accurate cost-effective characterization methods will be discussed. Finally, future perspectives of CMOS TDCs will be highlighted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".