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 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 3D-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 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".