Development of a Configurable Photon-to-Digital Converter in 65 nm
Bibliographic record
Abstract
A photo-detector chain with a timing jitter below 10 ps will increase the resolution of high-energy physics experiments vertex reconstruction, quantum telecommunication and medical imaging. To achieve this, our team at Université de Sherbrooke is developing a Photon-to-Digital Converter (PDC), an array of single photon avalanche diodes (SPAD) coupled one-to-one by their cathodes to quenching circuits (QC). The prototype, designed in TSMC 65 nm LP technology, includes the full front-end chain required to detect and timestamp photons. The prototype time-to-digital converter (TDC) adopted in our design is a patented cascaded-stage Vernier TDC with reduced power consumption and area already validated in 180 nm technology. The circuit includes 2 arrays, one optimized for rad-hard applications, of $4 \times 4$ QCs connected to integrated CMOS 65 nm SPADs as the input and to TDCs at the output. The QC discriminator consists of an inverter chain capable of configuring its switching voltage to optimize the front-end’s timing jitter and reduce spurious counts due to the input noise. Furthermore, the QC follows the technology’s design for manufacturing rules to maximize the production yield. This work follows the design process and shows updates on PDC fabrication and preliminary results on the newly developed QC prototype.
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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) | 0.003 | 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".