Embedded real time digital signal processing unit for a 64-channel PET detector module
Bibliographic record
Abstract
Recent developments in avalanche photodiode (APD) technology have led to the design and fabrication of a new radiation detector module based on an 8 × 8 array of LYSO crystals individually coupled to the pixels of two 4 × 8 monolithic APD arrays. This evolution entails the complete redesign of the data acquisition system to satisfy the 7-fold increase in pixel density relative to a previous implementation of individually read out sensors. As a result, the required digital signal processing cannot be implemented exclusively in FPGAs due to cost, area occupied and power consumption considerations. To comply with this new reality, a 64-channel mixed-signal ASIC, built from TSMC CMOS 0.18 μm technology, has been designed. It uses a Time-over-Threshold (ToT) scheme to extract both energy and timing along with the pixel number. A complex architecture of finite state-machines, driven by a 100 MHz clock, ensures the ASIC real time ToT calculation operations and its proper calibration by an external device. The ASIC can output as much as 2 Mevents/s on its LVDS data transfer dedicated link and consumes around 600 mW. The ASIC was developed following a mixed-signal flow allowing the designers to minimize and to verify the impact of undesirable parasitic effects on both analog and digital ends of the ASIC before sending the layout to the foundry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.010 | 0.003 |
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".