A 3.12 ps rms Ring Oscillator-Based Cascaded Vernier Time-to-Digital Converter with Two-Stages in TSMC 65 nm
Bibliographic record
Abstract
Our team is working on a photon-to-digital converter composed of an array of 4096 single-photon avalanche diodes (SPAD) each connected in 3D with a quenching circuit and 512 time-to-digital converters (TDC) in an 8-to-1 configuration. The target applications of this device are time-offlight (ToF) positron emission tomography, ToF computed tomography, quantum key distribution, and wavefront sensing for astronomy. A common requirement for these applications is a hightiming precision on the time of interaction of the photons. To this end, we developed a 43 μm × 35 μm TDC that utilizes a ring oscillator-based cascaded Vernier with 2 stages. In a test chip fabricated in TSMC 65 nm, composed of two 16 SPAD-quench arrays connected each to 8 TDCs in a 4-to-2 configuration. The TDC achieved a precision of 3.12 ps rms with a LSB of 8.23 ps for a dynamic range of 4 ns and a power consumption of 28.6 μW at 1 Mcps. We also detected a systematic nonlinearity issue in one of the TDC subcircuits. The presentation will discuss the advantages and design challenges of this TDC architecture, with an emphasis on the problematic subcircuit and the challenges of integrating TDCs in arrays.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".