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SCIENTIA SINICA Informationis, Volume 46, Issue 10: 1372-1391(2016) https://doi.org/10.1360/N112016-00085

Massively parallel computing in nano-VLSI interconnect modeling and lithography simulation

More info
  • ReceivedMay 4, 2016
  • AcceptedAug 4, 2016
  • PublishedOct 25, 2016

Abstract

VLSI is large in scale, and complex in structure. In today's nano-VLSI, serious process variations induced by the complex nanometer integrated circuit process technology may result in severe degradation of the integrated circuit performance. These factors present ever increasing challenges for present day nano-scale VLSI design. Interconnect modeling and lithography simulations rely on numerical approaches for solving large-scale Maxwell's equations, of which the computational cost is extremely high. In this paper, several massively parallel computing approaches for interconnect modeling and lithography simulation are surveyed, based on adaptive finite element theory and a parallel hierarchical grid (PHG) platform. Regarding interconnect modeling, we first review the parallel adaptive finite-element method ParAFEMCap for parasitic capacitance extraction, which achieves a parallel efficiency of 75.7\% on 1536 CPU cores. In addition, we review a hybridization of the boundary integral equation method and the random walk on spheres method (BIE-WOS) for surface charge density computations for conductors or dielectric mediums. The proposed method proves to be superior to existing methods for massively parallel computing. On a supercomputer with 5120 CPU cores, BIE-WOS can achieve almost a linear parallel efficiency. Regarding lithography simulation, we propose a parallel adaptive finite-element framework method by adopting the PHG (parallel hierarchical grid) platform and a perfectly matched anisotropy uniaxial layer to handle scattering boundary conditions.


Funded by

国家重点基础研究发展计划(973计划)

(2011CB309701)

国家自然科学基金(91430215)

国家自然科学基金(91530323)

国家自然科学基金(11321061)

中国科学院国家数学与交叉科学研究中心(NC MIS)

(2011CB309703)

国家高技术研究发展计划(863 计划)

(2012AA01A30901)

国家重点研发计划高性能计算重点专项项目(2016YFB0201304)

国家自然科学基金(61376040)

国家自然科学基金(61574046)

国家自然科学基金(91330201)

国家自然科学基金(61274032)


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