Wide Input Dynamic Range Fully Integrated Capacitive Sensor for Life Science Applications
Bibliographic record
Abstract
This paper presents a new fully integrated CMOS capacitance sensor chip with a wider input dynamic range (IDR) compared to the state-of-the-art, suitable for a variety of life science applications. With the novel differential capacitance to current conversion topology, it achieves an IDR of about seven times higher compared to the previous charge based capacitive measurement (CBCM) circuits and about three times higher compared to the CBCM with cascode current mirrors. It also features a calibration circuitry consisting of an array of switched capacitors, interdigitated electrodes (IDEs) realized on the topmost metal layer, a current-controlled 300 MHz oscillator, and a counter-serializer to create digital output. The proposed sensor, fabricated in AMS 0.35 μm CMOS technology, enables a high-resolution measurement, equal to 416 aF, of physiochemical changes in the IDE with up to 1.27 pF input offset adjustment range (IOAR). With a measurement speed of 15 μs, this sensor is among the fast CMOS capacitive sensors in the literature. In this paper, we demonstrate its functionality and applicability and present the experimental results for monitoring 2 μL evaporating droplets of chemical solvents. By using samples of solvents with different conductivity and relative permittivity, a wide range of capacitance and resistance variations in the sample-IDE interface electric equivalent model can be created. In addition, the evaporating droplet test has inherently fast dynamic changes. Based on the results, our proposed device addresses the challenge of detecting small capacitance changes despite large parasitic elements caused by the ions in the solution or by remnants deposited on the electrode.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".