A Low-Power Photon-Counter Front-End Dedicated to NIRS Brain Imaging
Bibliographic record
Abstract
This paper introduces a new miniaturized on-chip photodetector front-end targeted for portable near infrared spectroscopy as a noninvasive tool for real-time brain imaging. It includes silicon avalanche photodiodes (SiAPDs) with dual detection modes using a transimpedance amplifier (TIA) with on-chip gain/bias control, and a controllable mixed (active-passive) quench circuit, with tunable hold-off time, and a novel gated quench-reset technique. This integrated photoreceiver front-end has been fabricated using submicrometer standard CMOS technologies with a minimum fill-factor of 95%. Fabricated SiAPDs exhibit avalanche gains of 35 and 22 at 10 and 18 V bias voltages with red-shifted peak photon-detection efficiency and dark count-rates of 114 and 4 kHz (at 1 V excess bias voltage). The TIA consumes 1-mW power, and offers a transimpedance gain of 250 MV/A, a tunable bandwidth (1 kHz-1 GHz), and an input current referred noise <;10 fA/√Hz at 1 kHz. The photon-counter exhibits a quench-time of 10 ns with a 0.4-mW power-consumption with an adaptive hold-off time control. The on-chip integration of SiAPDs and front-end circuit, reduced the power-consumption and after-pulsing, and increased the sensitivity.
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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) | 0.001 | 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".