Development of Photon-to-Digital Converters -- A 3D Integrated Digital Single- Photon Detector
Bibliographic record
Abstract
We develop 3D integrated photon-to-digital converters (PDC) aimed to replace PMTs and SiPMs in various radiation science applications. In previous years we reported on a CMOS readout electronics designed for low power consumption in large area systems such as noble liquid dark matter or neutrino searches, or fast neutron spectroscopy. System integration of these PDCs has also been demonstrated. We also reported on the development of the SPAD technology optimized for precise timing at wavelengths below 500~nm and specifically designed to be 3D integrated onto the CMOS readout. A comprehensive test platform allows performing all SPAD testing in Geiger mode on either dies or wafers was also demonstrated. A revised CMOS readout modified to ease large system integration was designed. Wafers of this readout were produced and we are presently characterizing them to study performance variations on and across wafers. We also discuss the challenges of resizing and matching patterns between TSMC wafers and our custom SPAD layer. Recently, we have completed key milestones with wafer-to-wafer bonding of mock-up SPAD wafers onto resized but otherwise unprocessed TSMC CMOS wafers. We are proceeding with the bonding of functional SPAD wafers onto CMOS readout wafers to complete the first lot of 3D PDCs. We will report on their fabrication, on the CMOS readout operation, and on the SPAD performance.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".