Level-set shape reconstruction of binary permittivity distributions using near-field focusing capacitance measurements

Abstract

A near-field focusing capacitance sensor consists of an array of long, coplanar electrodes offset by a small interface gap from an identical orthogonal array of electrodes. The sensor may be used to characterize permittivity inhomogeneities in thin dielectric layers. The sensor capacitance measurements represent a tessellated matrix of integral-averaged values describing void content in a series of zones corresponding to the electrode crossing points (junctions) of the sensor. The sensor does not lend itself to computed tomography because the individual capacitance measurements do not represent overlapping regions of sensitivity. An evolving level-set algorithm is proposed to reconstruct a binary permittivity distribution. A mathematical construct, based on the physics of inverse-square fields, is used to approximately reconstruct shape features too small to be captured by the raw measurements. The method accommodates the non-uniform areasensitivity of the junction capacitance measurement. Effective use of the algorithm requires active management of the convergence criterion and evolution rate. The algorithm is demonstrated on a series of phantoms as well as measurements of a voided dielectric thermal interface material using a near-field focusing sensor.

Level-set reconstruction algorithm for ultrafast limited-angle X-ray computed tomography of two-phase flows

“…wherein F is a force function that determines the type of motion of the interface. It can be arbitrarily defined for different kinds of applications [12,13].…”

A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

“…Yang Y. J. and Peng L. H. proposed an enhanced linear model and sparsity regularization for the image reconstruction algorithm [ 33 ]. In other areas, Taylor S. H. and Garimella S. V. adopted a level set method to reconstruct ECT images [ 34 ]. Ren S. J. et al introduced the boundary element method for ECT image reconstruction, and this method was able to reconstruct the permittivity distribution profile in the imaging area well [ 35 ].…”

Capacitive sensing of local bond layer thickness and coverage in thermal interface materials

“…With this demonstration, both bond layer thickness and void content have been successfully characterized using the algorithm. It is noted that in previous work, 15 grayscale void fraction maps such as Figure 6c have been used to generate a pseudo-high-resolution binary image of the estimated void shape, [18].…”