  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.
