用于2.5D电分析的片外供电网络几何图形的有效分层离散化

M. Mondal, J. Pingenot, V. Jandhyala
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引用次数: 3

摘要

随着芯片内外日益复杂的环境的出现,电力输送网络(PDN)设计变得更加重要。多核芯片、具有多个参考电压的混合信号设计以及复杂的3D封装情况导致PDN几何形状复杂,具有高频需求和锐化边缘速率,即使在核心频率相对恒定的情况下也是如此。即使在求解器技术上有了最新的突破,这种pdn的早期设计迭代仍然是不可行的3D全波模拟器。对于设计合理的结构和中等频率,快速2.5D工具(特别是在多层有限差分法(MFDM)[1]的最先进形式中)工作良好。然而,将这些方法应用于包含切口、槽、过孔、微过孔和非笛卡尔形状的复杂布局是具有挑战性的。首先,从复杂的布局中生成一个分层一致的网格是非常重要的。其次,使用全局均匀网格划分,通常使用强制在不准确或低效的解决方案之间做出选择。本文提出了一种快速生成分层一致自适应矩形网格的算法,旨在显著提高2.5D方法的速度、容量和通用性。提出了一种基于自适应网格的MFDM的有效实现方法。结果表明,该算法具有足够的通用性,可以将这些方法从简单的pdn扩展到复杂的现实世界模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient hierarchical discretization of off-chip power delivery network geometries for 2.5D electrical analysis
Power delivery network (PDN) design is becoming even more critical with the advent of progressively complex environments on and beyond the die. Multicore chips, mixed-signal designs with multiple reference voltages, and complex 3D packaging situations lead to complicated PDN geometries with high-frequency demands and sharpening edge rates even when on-core frequencies are relatively constant. Early design iterations of such PDNs remain infeasible with 3D full-wave simulators even with the latest breakthroughs in solver technology. For reasonably well designed structures and moderate frequencies, fast 2.5D tools (particularly in the most evolved form of multilayered finite difference method (MFDM) [1]) work well. However, application of these methods to complex layouts containing cutouts, slots, vias, microvias and non-Cartesian shapes is challenging. First, generating a layer-wise consistent mesh from the complex layout is nontrivial. Second, use of globally uniform meshing, as generally employed forces a choice between an inaccurate or inefficient solution. This paper proposes an algorithm for rapidly generating layerwise consistent adaptive rectangular meshes for multilayered PDNs with the aim of significantly enhancing the speed, capacity and versatility of 2.5D methods. An efficient implementation of MFDM based on the adaptive mesh has been made. Results show that the algorithm is general enough to extend the applicability of these methods from simple PDNs to complex real-world models.
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