Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159505
Title: VoxCap: FFT-accelerated and Tucker-enhanced capacitance extraction simulator for voxelized structures
Authors: Wang, Mingyu
Qian, Cheng
White, Jacob K.
Yucel, Abdulkadir C.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Wang, M., Qian, C., White, J. K. & Yucel, A. C. (2020). VoxCap: FFT-accelerated and Tucker-enhanced capacitance extraction simulator for voxelized structures. IEEE Transactions On Microwave Theory and Techniques, 68(12), 5154-5168. https://dx.doi.org/10.1109/TMTT.2020.3022091
Project: 2018-T1-002-077 (RG 176/18)
Journal: IEEE Transactions on Microwave Theory and Techniques
Abstract: VoxCap, a fast Fourier transform (FFT)-accelerated and Tucker-enhanced integral equation simulator for capacitance extraction of voxelized structures, is proposed. The VoxCap solves the surface integral equations (SIEs) for conductor and dielectric surfaces with three key attributes that make the VoxCap highly CPU and memory efficient for the capacitance extraction of the voxelized structures: (i) VoxCap exploits the FFTs for accelerating the matrix-vector multiplications during the iterative solution of linear system of equations arising due to the discretization of SIEs. (ii) During the iterative solution, VoxCap uses a highly effective and memory-efficient preconditioner that reduces the number of iterations significantly. (iii) VoxCap employs Tucker decompositions to compress the block Toeplitz and circulant tensors, requiring the largest memory in the simulator. By doing so, it reduces the memory requirement of these tensors from hundreds of gigabytes to a few megabytes and the CPU time required to obtain Toeplitz tensors from tens of minutes (even hours) to a few seconds for very large scale problems. VoxCap is capable of accurately computing capacitance of arbitrarily shaped and large-scale voxelized structures on a desktop computer.
URI: https://hdl.handle.net/10356/159505
ISSN: 0018-9480
DOI: 10.1109/TMTT.2020.3022091
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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