High-DR CMOS Fluorescence Biosensor With Extended Counting ADC and Noise Cancellation
Bibliographic record
Abstract
Accurately resolving small fluorescence power variations in presence of noise and high-background tissue autofluorescence from deep brain structures with a fiber photometry system requires highly linear and sensitive photo detectors. This paper presents a high-dynamic range (DR) CMOS biosensor fusing a low-noise photosensing front-end with a high-precision extended counting analog-to-digital converter (ADC) with noise cancellation to detect florescence neural signal fluctuations of very low incident power. The 7 MSBs are resolved by a first order continuous-time resettable ΣΔ ADC, whereas the residue voltage is quantized by a 10-bit single slope ADC for enabling wide-dynamic range and high-precision fluorescence sensing. The reset noise is canceled out by an embedded noise cancellation scheme which is subtracting the reset noise from the signal using a correlated double sampling scheme. Unlike other solutions, the biosensor has a short conversion time of 306.5 μs compared to a typical fluoresence sampling period of 10 ms, providing a very low duty cyle of 3%, which is a key to achieve low excitation source power consumption in this application to extend system autonomy, and to avoid photobleaching and phototoxicity in the tissue. The proposed optoelectronic biosensor is implemented in a 0.18-μm CMOS technology, consuming 93 μW from a 3.3-V supply voltage while achieving a DR of 104 dB, a minimum detectable current of 1.3 pArms, and a chip area of 0.475 mm2. We present the measured performance of the biosensor using an optical experimental setup including a LED driver, a fiber optic, and a test board.
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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 | 0.001 | 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".