Performance of 3D Photon-to-Digital Converters
Bibliographic record
Abstract
Photon-to-digital converter (PDC) detectors are versatile photodetector arrays based on singlephoton avalanche diode (SPAD). They are adaptable to multiple applications, like medical imaging, high-energy physics experiments and lidar, among others. Renowned for their high sensitivity and subnanosecond timing resolution performance, they are suitable for large-scale, low-cost detection infrastructures where the compactness of their data readout can be leveraged. The development of 3D vertically integrated PDC (3D PDC) is ongoing for some years at Universite de Sherbrooke. With its custom$64 \times 64$SPAD array technology, leveraging Teledyne Dalsa's MEMS and CCD capabilities for deep trench isolation and high optical performance, coupled with CMOS electronics readout integrated circuit design (TSMC CMOS 180 nm), the 3D PDCs are designed using standardized industrial foundries processes for large-scale manufacturing capability. Two 3D PDC production lots have been completed since October 2024. We report on performance assessment at die level and wafer level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".