An Energy-Efficient CMOS Biophotometry Sensor With Incremental DT-∑Δ ADC Conversion
Bibliographic record
Abstract
This paper presents a high-sensitivity incremental discrete time <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Σ</b> Δ analog-to-digital converter (ADC) with variable clock and on-chip decimation for biophotometry sensing. A digital correlated double sampling (CDS) scheme is merged with the decimation filter for providing energy- and area-efficient mean to suppress low-frequency noise. Chopper modulation and differential sensing using a dummy photodiode along with charge transfer switches are used as well to allow for the detection of ultra low-input low fluorescence light, down to a few femttowatt, by suppressing the low-frequency noise of the <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Σ</b> Δ modulator and the dark current of the photodetector. A variable clock scheme is used across the sensor operating phases (namely the reset conversion phase and the signal conversion phase) to decrease the biosensor conversion time. To decrease illumination time and save the excitation light source energy, the biosensor uses a short sensing duty cycle of 1.2 ms (12%) for a sampling period of 10 ms. The proposed optoelectronic biosensor is implemented in a 0.18-μm CMOS technology, consuming 56 μW from a 3.3/1.8-V supply voltage, while achieving a high signal-to-noise and distortion ratio of 101 dB and a minimum detectable current of <; 100-fArms, within conversion time of 1.2 ms. The proposed biosensor presents a FOM of 0.73 pJ/conv., which is among the best reported performances compared to previous solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".