In-Chip Data Processing and Readout Architecture of a Photon-to-Digital Converter in 65 nm For Crystal-Based Applications
Bibliographic record
Abstract
Our team is designing a 5×5 mm23D-integrated Photon-to-Digital Converter (PDC) comprising a 64×64 SPAD array with a custom 65 nm CMOS readout. The PDC is partitioned into 16 sectors (1.25 × 1.25 mm2), each holding 256~SPADs and 64~time-to-digital converters (TDCs) with an 8-to-1 ratio. At high event rates, the unprocessed data stream exceeds the available bandwidth of 2 Gbps (CML transceiver) resulting in sector-level deadtime. To mitigate this, we are developing a processing architecture featuring parallel time and energy processing chains with early data reduction. The time chain performs skew correction, dark count suppression, timestamp alignment, and sorting. A linear unbiased estimator further compresses timing data into a single timestamp. The energy chain sums trigger counts over configurable time windows to detect scintillation events. A binning module further compresses time and energy data through energy-discriminated binning at the sector level. The modular architecture accommodates multiple acquisition modes (synchronous, asynchronous, camera) and output formats (timestamps, histograms, hitmaps, heatmaps). This architecture is capable of addressing applications such as time-of-flight (TOF) positron emission tomography (TOF-PET) and time-of-flight computed tomography (TOF-CT), as well as other non-scintillation-based applications such as quantum key distribution and wavefront sensing for adaptive optics.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".