Design considerations for embedded real-time processing for 3D digital SiPMs with multiple TDCs
Bibliographic record
Abstract
The timing performance of digital silicon photomultipliers (dSiPMs) detectors have placed them as serious candidates for time-of-flight (TOF) measurement in positron emission tomography (PET). Contributors to timing uncertainty in current dSiPM scintillation detectors include: 1) routing skew and array non-uniformities, 2) SPAD dark counts and 3) scintillator statistics. A photodetector with a one-to-one coupling of timeto- digital converters (TDCs) and single photon avalanche diodes (SPADs) is under study as it opens new methods to minimize the impact these contributors. However, the number of TDCs required for this coupling scheme generates a tremendous amount of data. Acknowledging these design considerations, a real-time processing scheme is proposed to minimize the bandwidth while improving the timing resolution by 1) correcting routing skews and array non-uniformities, 2) filtering dark counts and 3) estimating the time of interaction from multiple photoelectron timestamps. Simulations were conducted to evaluate the impact of the contributors to timing uncertainty and the efficiency of the proposed architecture. With a simulated 1 x 1 x 10 mm3LYSO scintillator, the coincidence timing resolution is improved from 161 ps to 115 ps with the simulated detector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".