Wafer-Level Characterization and Monitoring Platform for Single-Photon Avalanche Diodes
Bibliographic record
Abstract
When developing a technology based on single-photon avalanche diodes (SPADs), the SPAD characterization is mandatory to debug, optimize and monitor the microfabrication process. This is especially true for the development of SPAD arrays 3D integrated with CMOS readout electronics, where SPAD testing is required to qualify the process, independently from the final CMOS readout circuit. This work reports on a characterization and monitoring platform dedicated to SPAD testing at die and wafer level, in the context of a 3D SPAD technology development. The platform relies on a dedicated integrated circuit made in a standard CMOS technology and used in different configurations from a prototype printed circuit board (die-level testing) to active probe cards (wafer-level mapping). The platform gives full access to SPAD characteristics in Geiger mode such as the dark noise, photon detection efficiency and timing resolution. The integrated circuit and its configuration are described in detail as well as results obtained on different SPAD test structures. In particular, the dark count rate mapping demonstrates the benefits of testing SPADs at wafer level at the R&D stage.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".