Measuring Dark Currents in Multiple Cryogenic SiPMs With Sub-pA Sensitivity Using an Automated <i>I–V</i> Multiplexer
Bibliographic record
Abstract
We present the design of an automated current-voltage (IV) multiplexer (MUX) that enables accurate measurement of the dark current in cryogenic silicon photomultipliers (SiPMs), achieving a sensitivity equivalent to detecting less than one avalanche per second. Dynamic pulse-counting measurements were used as a benchmark for reconstructing the dark current in static IV measurements. The IV-MUX features 15 channels on a single board and up to seven boards can be connected in parallel under the control of one Arduino microcontroller. To minimize leakage and enhance performance, the layout includes guard rings and high-isolation relays, enabling resolution of currents as small as 49 fA. The IV-MUX can be integrated into systems designed for IV or pulse-counting measurements, enabling seamless switching between IV and pulse-counting modes. Moreover, the IV-MUX is vacuum-compatible, validated by testing an SiPM array in a cryostat. This feature reduces the need for multiple feedthroughs when testing sensor arrays in vacuum. The design is open source and can be used to facilitate rapid and automated testing of SiPMs or similar low-current devices in one measurement cycle.
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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 | 0.000 |
| 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".