A Wireless Fiber Photometry System Based on a High-Precision CMOS Biosensor With Embedded Continuous-Time $\Sigma \Delta$ Modulation
Bibliographic record
Abstract
Fluorescence biophotometry measurements require wide dynamic range (DR) and high-sensitivity laboratory apparatus. Indeed, it is often very challenging to accurately resolve the small fluorescence variations in presence of noise and high-background tissue autofluorescence. There is a great need for smaller detectors combining high linearity, high sensitivity, and high-energy efficiency. This paper presents a new biophotometry sensor merging two individual building blocks, namely a low-noise sensing front-end and a order continuous-time modulator (CTSDM), into a single module for enabling high-sensitivity and high energy-efficiency photo-sensing. In particular, a differential CMOS photodetector associated with a differential capacitive transimpedance amplifier-based sensing front-end is merged with an incremental order 1-bit CTSDM to achieve a large DR, low hardware complexity, and high-energy efficiency. The sensor leverages a hardware sharing strategy to simplify the implementation and reduce power consumption. The proposed CMOS biosensor is integrated within a miniature wireless head mountable prototype for enabling biophotometry with a single implantable fiber in the brain of live mice. The proposed biophotometry sensor is implemented in a 0.18- CMOS technology, consuming from a 1.8- supply voltage, while achieving a peak dynamic range of over a 50- input bandwidth, a sensitivity of 24 mV/nW, and a minimum detectable current of 2.46- at a 20- sampling rate.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".