A Novel Calibration-Free Fully Integrated CMOS Capacitive Sensor for Life Science Applications
Bibliographic record
Abstract
CMOS capacitive sensors for label-free monitoring of biological/chemical reactions are typically prone to inaccuracies due to the parasitic elements and mismatch rooted in the CMOS fabrication process as well as real-time changes inside the sample solution. The former can usually be compensated by employing differential circuits and static calibration of the sensor before the experiment. On the other hand, changes in the sample solution such as sedimentation of non-target molecules or change in the conductivity of solution can significantly alter the operating point and result in inaccurate sensor readings that might require recalibration of the sensor during the experiment. In this paper, we present a CMOS capacitive sensor that is calibration-free via the creation of time-resolved three-dimensional (3D) surface electrochemical profiles. These 3D profiles uncover the variations of both target and unwanted parasitic capacitances. The sensor includes on-chip interdigitated electrodes (IDEs), a wide input dynamic range (IDR) differential capacitance-to-current converter, a digitally programable reference capacitor, and an oscillator-based analog-to-digital converter (ADC), and is implemented using 0.35 µm AMS CMOS process. The IDR covers a change in capacitance as small as 1 fF up to 1.27 pF with a minimum detectable change of 0.416 fF.
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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 | 0.002 |
| 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".