Microscale 3-D Capacitance Tomography with a CMOS Sensor Array

TL;DR

This paper demonstrates high-resolution 3-D capacitance tomography of microscale structures using a CMOS sensor array, combined with deep learning for improved reconstruction accuracy.

Contribution

It introduces a CMOS-based ECT system for microscale imaging and a novel deep learning approach for accurate 3-D permittivity reconstruction.

Findings

Achieved 10-micron spatial resolution in ECT imaging.

Attained over 91% prediction accuracy on microsphere data.

Improved reconstruction accuracy by 4.6% over baseline methods.

Abstract

Electrical capacitance tomography (ECT) is a nonoptical imaging technique in which a map of the interior permittivity of a volume is estimated by making capacitance measurements at its boundary and solving an inverse problem. While previous ECT demonstrations have often been at centimeter scales, ECT is not limited to macroscopic systems. In this paper, we demonstrate ECT imaging of polymer microspheres and bacterial biofilms using a CMOS microelectrode array, achieving spatial resolution of 10 microns. Additionally, we propose a deep learning architecture and an improved multi-objective training scheme for reconstructing out-of-plane permittivity maps from the sensor measurements. Experimental results show that the proposed approach is able to resolve microscopic 3-D structures, achieving 91.5% prediction accuracy on the microsphere dataset and 82.7% on the biofilm dataset, including an average of 4.6% improvement over baseline computational methods.