VoxCap: FFT-Accelerated and Tucker-Enhanced Capacitance Extraction Simulator for Voxelized Structures
Name
2004.02609.pdf
Description
Accepted version
Size
2.89 MB
Format
Adobe PDF
Checksum (MD5)
f643d33467edeafadfb8fa3ab51ada90
Author(s)
Wang, Mingyu
Qian, Cheng
White, Jacob K
Yucel, Abdulkadir C
Date Issued
2020
Journal
IEEE Transactions on Microwave Theory and Techniques
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
© 1963-2012 IEEE. 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 the 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: 1) VoxCap exploits the FFTs for accelerating the matrix-vector multiplications during the iterative solution of the linear system of equations arising due to the discretization of SIEs; 2) during the iterative solution, VoxCap uses a highly effective and memory-efficient preconditioner that reduces the number of iterations significantly; and 3) VoxCap employs the 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 the 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. VoxCap's accuracy, efficiency, and capability are demonstrated through capacitance extraction of various large-scale structures, including the parallel meander lines discretized by more than a hundred million panels and analyzed on a commodity desktop computer.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TMTT.2020.3022091
