A Fine Resolution TDC Architecture for Next Generation PET Imaging
Bibliographic record
Abstract
A fine resolution and process scalable CMOS time-to-digital converter (TDC) architecture is presented. A 6-bit fine resolution TDC design using the new architecture is evaluated for positron emission tomography (PET) imaging application. The TDC architecture uses a hierarchical delay processing structure to achieve single cycle latency and high speed of operation. The fine resolution converter, realized in 130 nm CMOS, is designed to operate over a reference clock frequency of 500 MHz but can be scaled to multi GHz operation through time interleaving. Without external calibration, the TDC is used as a 5-bit fine resolution converter with 4.65 ENOB (effective number of bits). Under this condition, the 6-bit TDC has an INL (integral non-linearity) measurement of less than 1.45 LSB and a DNL (differential non-linearity) measurement of less than 1.25 LSB. With external calibration, a reduction of more than 50% in INL/DNL nonlinearities is demonstrated improving the ENOB to 5.5 bits, pushing the TDC to a 6-bit fine resolution operation. The TDC has a 31 ps timing resolution and power consumption of less than 1 mW. The design is believed to be the fastest and the lowest power consuming fine resolution TDC in the literature.
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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 | 0.001 |
| 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".