Calibration-Free CMOS Capacitive Sensor for Life Science Applications
Bibliographic record
Abstract
CMOS capacitive sensors reported for high accuracy cellular molecular measurements typically suffer from significant parasitic capacitance changes caused by remnants and sediments during the experiment with several biological and chemical reactions. In this paper, we propose a novel calibration-free capacitive sensing system that addresses this problem. The proposed CMOS capacitive sensor includes interdigitated electrodes (IDEs), a capacitance-to-current converter with a wide input dynamic range (IDR), a variable reference capacitor, and an oscillator-based analog-to-digital converter (ADC) which has been fabricated using 0.35 μm AMS CMOS process. Sweeping the value of the variable reference capacitor from 0.1 fF up to 1.27 pF with a step of 10 fF and repeating the sweep each second during the experiment allows the creation of time-resolved three-dimensional (3D) fingerprints for the measurement of capacitance variations of the sample-electrode interface resulted from both the target material as well as non-target parasitic capacitances. We have tested the sensor using three different chemical solvents. The four different categories of curves that constitute the fingerprints of the chemicals showed a match with the post-layout simulation results. Capacitance change in the range of 0.416 fF up to 1.27 pF can practically be monitored. The electrode area of 110 μm by 220 μm and the micrometer chamber size allows for placing tiny droplets of a few microliters. The generated fingerprint is valid for the chemicals with a conductivity of up to 5 mS/cm.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".