Fast methods for full-wave electromagnetic simulations of integrated circuit package modules

Fast methods for the electromagnetic simulation of integrated circuit (IC) package modules through model order reduction are demonstrated. The 3D integration of multiple functional IC chip/package modules on a single platform gives rise to geometrically complex structures with strong electromagnetic phenomena. This motivates our work on a fast full-wave solution for the analysis of such modules, thus contributing to the reduction in design cycle time without loss of accuracy. Traditionally, fast design approaches consider only approximate electromagnetic effects, giving rise to lumped-circuit models, and therefore may fail to accurately capture the signal integrity, power integrity, and electromagnetic interference effects. As part of this research, a second order frequency domain full-wave susceptance element equivalent circuit (SEEC) model will be extracted from a given structural layout. The model so obtained is suitably reduced using model order reduction techniques. As part of this effort, algorithms are developed to produce stable and passive reduced models of the original system, enabling fast frequency sweep analysis. Two distinct projection-based second order model reduction approaches will be considered: 1) matching moments, and 2) matching Laguerre coefficients, of the original system's transfer function. Further, the selection of multiple frequency shifts in these schemes to produce a globally representative model is also studied. Use of a second level preconditioned Krylov subspace process allows for a memory-efficient way to address large size problems.Ph.D.Committee Chair: Swaminathan Madhavan; Committee Member: Papapolymerou John; Committee Member: Chatterjee Abhijit; Committee Member: Peterson Andrew; Committee Member: Sitaraman Sures

The impact of global communication latency at extreme scales on Krylov methods

Krylov Subspace Methods (KSMs) are popular numerical tools for solving large linear systems of equations. We consider their role in solving sparse systems on future massively parallel distributed memory machines, by estimating future performance of their constituent operations. To this end we construct a model that is simple, but which takes topology and network acceleration into account as they are important considerations. We show that, as the number of nodes of a parallel machine increases to very large numbers, the increasing latency cost of reductions may well become a problematic bottleneck for traditional formulations of these methods. Finally, we discuss how pipelined KSMs can be used to tackle the potential problem, and appropriate pipeline depths

Block oriented model order reduction of interconnected systems

Unintended and parasitic coupling effects are becoming more relevant in currently designed, small-scale/highfrequency RFICs. Electromagnetic (EM) based procedures must be used to generate accurate models for proper verification of system behaviour. But these EM methodologies may take advantage of structural sub-system organization as well as information inherent to the IC physical layout, to improve their efficiency. Model order reduction techniques, required for fast and accurate evaluation and simulation of such models, must address and may benefit from the provided hierarchical information. System-based interconnection techniques can handle some of these situations, but suffer from some drawbacks when applied to complete EM models. We will present an alternative methodology, based on similar principles, that overcomes the limitations of such approaches. The procedure, based on structure-preserving model order reduction techniques, is proved to be a generalization of the interconnected system based framework. Further improvements that allow a trade off between global error and block size, and thus allow a better control on the reduction, will be also presented

Modellierung, Simulation und Optimierung integrierter Schaltkreise

[no abstract available

Bordered Block-Diagonal Preserved Model-Order Reduction for RLC Circuits

This thesis details the research of the bordered block-diagonal preserved model-order reduction (BVOR) method and implementation of the corresponding tool designed for facilitating the simulation of industrial, very large sized linear circuits or linear sub-circuits of a nonlinear circuit. The BVOR tool is able to extract the linear RLC parts of the circuit from any given typical SPICE netlist and perform reduction using an appropriate algorithm for optimum efficiency. The implemented algorithms in this tool are bordered block-diagonal matrix solver and bordered block-diagonal matrix based block Arnoldi method

Field solver technologies for variation-aware interconnect parasitic extraction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 207-213).Advances in integrated circuit manufacturing technologies have enabled high density onchip integration by constantly scaling down the device and interconnect feature size. As a consequence of the ongoing technology scaling (from 45nm to 32nm, 22nm and beyond), geometrical variabilities induced by the uncertainties in the manufacturing processes are becoming more significant. Indeed, the dimensions and shapes of the manufactured devices and interconnect structures may vary by up to 40% from their design intent. The effect of such variabilities on the electrical characteristics of both devices and interconnects must be accurately evaluated and accounted for during the design phase. In the last few years, there have been several attempts to develop variation-aware extraction algorithms, i.e. algorithms that evaluate the effect of geometrical variabilities on the electrical characteristics of devices and interconnects. However, most algorithms remain computationally very expensive. In this thesis the focus is on variation-aware interconnect parasitic extraction. In the first part of the thesis several discretization-based variation-aware solver techniques are developed. The first technique is a stochastic model reduction algorithm (SMOR) The SMOR guarantees that the statistical moments computed from the reduced model are the same as those of the full model. The SMOR works best for problems in which the desired electrical property is contained in an easily defined subspace.(cont.) The second technique is the combined Neumann Hermite expansion (CNHE). The CNHE combines the advantages of both the standard Neumann expansion and the standard stochastic Galerkin method to produce a very efficient extraction algorithm. The CNHE works best in problems for which the desired electrical property (e.g. impedance) is accurately expanded in terms of a low order multivariate Hermite expansion. The third technique is the stochastic dominant singular vectors method (SDSV). The SDSV uses stochastic optimization in order to sequentially determine an optimal reduced subspace, in which the solution can be accurately represented. The SDSV works best for large dimensional problems, since its complexity is almost independent of the size of the parameter space. In the second part of the thesis, several novel discretization-free variation aware extraction techniques for both resistance and capacitance extraction are developed. First we present a variation-aware floating random walk (FRW) to extract the capacitance/resistance in the presence of non-topological (edge-defined) variations. The complexity of such algorithm is almost independent of the number of varying parameters. Then we introduce the Hierarchical FRW to extract the capacitance/resistance of a very large number of topologically different structures, which are all constructed from the same set of building blocks. The complexity of such algorithm is almost independent of the total number of structures. All the proposed techniques are applied to a variety of examples, showing orders of magnitude reduction in the computational time compared to the standard approaches. In addition, we solve very large dimensional examples that are intractable when using standard approaches.by Tarek Ali El-Moselhy.Ph.D