Capacitance Measurement

This research area focuses on advancing low-frequency nanoscopic measurement techniques to accurately quantify the dielectric properties of thin films at submicron spatial resolutions. Accurate dielectric-constant mapping at this scale is critical for the development of micro- and nanoelectronic devices, where local film properties significantly influence device behavior. Challenges include isolating local capacitance from stray capacitance and accounting for tip geometry effects in scanning probe methods.

This theme investigates experimental protocols and data interpretation approaches for measuring electrode capacitance, particularly for energy storage materials like supercapacitors. Distinct assumptions in CV and EIS regarding ideality, frequency dependence, and presence of constant phase elements (CPE) complicate the direct comparison of capacitance values. The research addresses the non-ideal capacitive behavior and aims to reconcile capacitance measurements through modified models accounting for frequency dispersion and fractional-order dynamics.

This theme centers on the development and application of charge-based capacitance measurement (CBCM) and crosstalk-based capacitance measurement (CTCM) methods for precise evaluation of on-chip wiring and coupling capacitances. As scaling diminishes feature sizes, coupling capacitances increasingly affect device performance and signal integrity. CBCM and CTCM provide scalable, high-resolution techniques for isolating mutual capacitances in complex test structures, enabling improved process monitoring, modeling, and yield analysis in advanced semiconductor technologies.

This research theme explores advanced capacitance sensing methods combined with machine learning approaches, specifically optimized artificial neural networks (ANN), to enhance liquid level measurements. By integrating ANN with capacitance level sensors, these techniques dynamically linearize sensor outputs and adapt to changing permittivity and temperature conditions without requiring repeated recalibration. The approach benefits automated and robust sensing in industrial and process control applications subject to variable environmental factors.

This area investigates capacitance spectroscopy as a promising electrochemical approach for non-invasive quantification of blood sugar by detecting changes in electrical properties (capacitance, permittivity) related to glucose concentration. It addresses challenges in sensitivity, lag time between blood glucose changes and capacitance response, and system optimization through electrode design and measurement frequency. The method's simplicity, low cost, and moderate correlation with invasive references suggest potential for clinical and wearable glucose monitoring.

The research focuses on optimizing capacitive sensor geometries and parameters using finite element modeling to enhance sensitivity and lower detection limits for nanometer-scale particulate pollutants. This addresses challenges in sensing ultrafine carbon particulates in exhaust gases, where conventional sensors lack mass measurement capabilities or sufficient sensitivity. Design optimization considers electrode spacing, insulation thickness, and electrode configuration to enable real-time particulate mass detection critical for environmental monitoring and regulatory compliance.

This research investigates the use of high-density capacitive sensor matrices combined with ring oscillator-based pixel readout circuits for nondestructive probing of biological cells in microfluidic environments. The approach aims to detect minute dielectric contrast from distinct cell types and geometries, enabling real-time cell positioning, identification, and morphology estimation. Leveraging CMOS integration and sophisticated signal processing algorithms, the technique reduces complexity and improves sensitivity over traditional optical microscopy methods.

Focusing on dynamic estimation of capacitor model parameters essential for health monitoring and fault diagnosis, this theme explores algorithmic observers implemented on microcontrollers that estimate equivalent capacitance and series resistance from voltage measurements during capacitor discharge. Real-time parameter extraction enables proactive condition monitoring for capacitors in power electronics and renewable energy systems, improving reliability and maintenance decision-making.

This theme addresses design principles, sensor development, and reconstruction algorithms for ECT and ECVT systems to non-intrusively image dielectric permittivity distributions in multiphase flows, critical for optimizing industrial processes in energy systems. Research advances focus on increasing spatial/temporal resolution, robust sensor design under harsh conditions, and computational techniques to solve inverse problems, facilitating real-time 3D visualization and quantitative flow characterization.