A Fully Integrated Silicon Photonics-based DAQ for a Cryogenic Large Scale Particle Physics Experiment
Bibliographic record
Abstract
Future particle physics experiments such as nEXO and ARGO have will dedicated state-of-the-art electronics for their detector. As this electronics will most likely be in the cryogenic environment, major challenges arise for their design from the detector readout to data communication. Their requirements for radiopurity and chemical constitution add further constraints on the type of usable materials. This paper presents a silicon photonics based communication system for those experiments. This system aims to be fully integrated within a photodetection module, connecting large arrays of photon-to-digital converters to a large-scale data acquisition system. The novelty of the design relies on having no laser source in the cryostat, a major benefit for power consumption. This approach leads to a set of considerations for the design of each component of the system and the need of designing a system in a comprehensive « top-down » approach to ensure the compatibility of the components. Key challenges related to cryogenic operation of the devices are highligthed alongside our approach to move from the prototype stage towards a complete system. We present the design of the system architecture, the design of the SiP chip (AMF foundry), the design and simulation results of 65 nm TSMC CMOS SiP Driver, the design and bandwidth results of the FPGA DAQ integration and the outline of a packaging solution for optical communication within cryogenic experiments with an overview of the remaining challenges to overcome.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".