Fully On-Chip Integrated Photodetector Front-End Dedicated to Real-Time Portable Optical Brain Imaging
Bibliographic record
Abstract
Optical brain imaging using functional near infra-red spectroscopy (fNIRS) offers a portable and noninvasive tool for monitoring of blood oxygenation. In this paper we have introduced a new miniaturized photodetector front-end on achip to be applied in a portable fNIRS system. It includes silicon avalanche photodiodes (SiAPD), Transimpedance amplifier (TIA) front-end and Quench-Reset circuitry to operate in both linear and Geiger modes. So it can be applied for both continuous-wave fNIRS (CW-fNIRS) and also single-photon counting. Proposed SiAPD exhibits high-avalanche gain (>100), low-breakdown voltage ( V) and high photon detection efficiency accompanying with low dark count rates. The proposed TIA front-end offer a low power consumption ( mW), high-transimpedance gain (up to 250 MV/A), tunable bandwidth (1 kHz - 1 GHz) and very low input and output noise (~few fA/√Hz and few μV/√Hz). The Geiger-mode photon counting front-end also exhibits a controllable hold-off and rest time with an ultra fast quench-reset time (few ns). This integrated system has been implemented using submicron (0.35 μm) standard CMOS technology.
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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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".