Photon-to-Digital Converter in 65 nm CMOS Technology with SPTR Below 15 ps FWHM
Bibliographic record
Abstract
The advancement of high-energy physics, medical imaging and quantum communication relies on precise timing and spatial photon detection. We are developing a photon-to-digital converter (PDC), consisting of an array of single-photon avalanche diodes (SPADs) connected one-to-one at the cathode with quenching circuits (QCs). The PDC, implemented in TSMC 65 nm LP CMOS technology, will integrate 4096 pixels and the readout chain required for single-photon detection and time tagging. The time-stamping will be achieved through 512 tiled time-to-digital converters (TDC), using a cascaded-stage vernier architecture which offers low power and area while keeping a 10 ps timing resolution. The future PDC design is based on preliminary results from last year's prototype showing single-photon timing resolution (SPTR) values below 15 ps FWHM when the QC is directly connected to a 2D SPAD. This confirms the effectiveness of the readout architecture and circuit-level implementation. The timing precision and granularity of the PDC leads to a great versatility. By analyzing the different applications the PDC is designed for, the presentation will delve in the architectural challenges and capabilities of our device.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".