A high-precision CMOS biophotometry sensor with noise cancellation and two-step A/D conversion
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 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 high-dynamic range CMOS photodetector embedding a photosensor and a high-precision two-step analog-to-digital converter (ADC) with a noise cancellation scheme. In this system, a 16-bit two-step ADC successively uses an integrating ADC and a successive approximation register (SAR) ADC enabling wide dynamic range and high energy-efficiency photocurrent quantization. Noise cancellation is achieved through a SAR digital-to-analog (DAC) capacitor bank to store and subtract the low-frequency noise from the output of a capacitive transimpedance amplifier (CTIA) throughout each data conversion. The 6-most significant bits are resolved through the integrating ADC, while the 10-least significant bits are extracted by the SAR ADC. The two-step data converter uses a hardware sharing scheme to decrease the chip size and to improve energy-efficiency. The proposed optoelectronic detector is implemented in a 0.18-μm CMOS technology, consuming 60 μW from a 3.3-V supply voltage while achieving a DR of 94 dB, a minimum detectable current of 200-f Arms, at 1-kS/s sampling rate. The proposed biosensor presents a FOM of 1.46 pJ/conv. which is among the best reported performance among similar systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".