Implementation Study of Single Photon Avalanche Diodes (SPAD) in <formula formulatype="inline"><tex Notation="TeX">$0.8~\mu\hbox{m}$</tex> </formula> HV CMOS Technology
Bibliographic record
Abstract
Single Photon Avalanche Diodes (SPAD) are known for their excellent timing performance which enables Time of Flight capabilities in positron emission tomography (PET). However, current array architectures juxtapose the SPAD with its ancillary electronics at the expense of a poor fill factor of the SPAD array. The 3D vertical integration of SPADs and readout electronics represents a solution to the aforementioned problem. Compared to systems with external electronics readout, 3D vertical integration reduces the SPAD interconnect parasitic capacitance while greatly increasing the photosensitive area and improving overall performances. This paper presents the implementation of two SPAD structures designed for PET. The SPAD structures are designed using Teledyne DALSA high voltage (HV) CMOS technology targeted for a 3-dimensional single photon counting module (3DSPCM). SPAD with two types of guard ring (diffusion-based and virtual guard ring) are designed, fabricated and characterized. All structures are based on a p + anode in an n-well cathode and are implemented along with active quenching circuits for proper characterization. The results show that the contact distribution and the anode-cathode spacing impact the dark count rate (DCR). The design of SPADs with a diffusion guard ring have a DCR down to 3 s- 1μm-2at room temperature, afterpulsing probability of , timing resolution of 27 ps FWHM and PDE of 49% at 480 nm.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".