High-resolution flash time-to-digital conversion and calibration for system-on-chip testing
Bibliographic record
Abstract
Verification of timing performance in systems-on-chip (SoCs) is becoming more difficult as clock frequencies and levels of integration increase. As a result, on-chip timing measurement has become a very attractive alternative for validation of these systems because it helps to overcome the bandwidth and test access limitations inherent in SoC environments. Flash time-to-digital converters (TDCs) are well suited for use in on-chip timing measurement systems because they can be operated at high speeds, offer low test time and are relatively easy to integrate. However, clock jitter in modern SoCs is often of the same order of magnitude as the temporal resolution of the TDC itself. Therefore, techniques are required to increase TDC resolution while ensuring timing accuracy. A high-resolution flash TDC is presented that exploits the random offsets on flip-flops or arbiters to perform time quantisation. Also described is a novel technique based on additive temporal noise to accurately calibrate this measurement device. Simulation and experimental results reveal that the latter method can calibrate the high-resolution flash TDC down to 5 ps within reasonable error limits. In addition, accurate timing measurement of jitter below 10 ps has been experimentally validated using a high-resolution flash TDC fabricated in a 0.18-µm CMOS process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".