A 3D Photon-to-Digital Converter Readout for Low-Power and Large-Area Applications
Bibliographic record
Abstract
The new trend in large area noble liquid experiments is to measure the scintillation light with photodetectors and their electronics inside the active volume. Compared to the typical approach of using silicon photomultipliers (SiPM) with the analog readout chain to an analog-todigital converter, this paper presents a new 3D photon-to-digital converter (PDC) readout that takes advantage of the binary nature of the single-photon avalanche diode. The readout contains 4096 pixels over 25 mm 2 , each including a 3D bonding pad and a quenching circuit. The readout features three different outputs : a fast flag to get the timestamp of each event from an external time-to-digital converter, a digital sum to retrieve the number of pixels triggered during an event and an analog monitor to generate an analog SiPM-like output. The analog monitor is also used to validate the two former digital outputs. The readout also includes 61 2D CMOS SPADs for validation purpose prior to the final 3D integration with a Teledyne DALSA custom SPAD array. As a first system integration toward large-area detector applications, a mini-tile of 2 × 2 readouts has been developed to test all the functionalities. The measured single-photon timing resolution ranges from 72 to 93 ps FWHM across the mini-tile SPAD channels population (i.e. 4 × 61 channels). The flag timing resolution is below 95 ps RMS, which includes the contribution of the optimized flag H-tree with an additional trigger tree to replace the 3D SPAD array for 2D testing. Once bonded with the 3D SPADs, the trigger tree won't be required to measure the flag timing resolution. With the removed contribution of the trigger tree, the estimated flag timing resolution should be below 45 ps RMS. The benefits of the digital sum output depend on the application, and this paper focuses on two cases. First, a low-power coincidence scheme for the nEXO liquid xenon experiment, leading to a power consumption as low as 140 µW per PDC. On the other hand, with a finer sampling mode of the scintillation light for pulse shape discrimination in liquid argon, the power consumption remains below 100 µW per PDC. Overall, this readout is designed as a replacement for a typical analog SiPM chain, without any compromise on the performances.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".