Variable-fidelity electromagnetic simulations and co-kriging for accurate modeling of antennas

Accurate and fast models are indispensable in contemporary antenna design. In this paper, we describe the low-cost antenna modeling methodology involving variable-fidelity electromagnetic (EM) simulations and co-Kriging. Our approach exploits sparsely sampled accurate (high-fidelity) EM data as well as densely sampled coarse-discretization (low-fidelity) EM simulations that are accommodated into one model using the co-Kriging technique. By using coarse-discretization simulations, the computational cost of creating the antenna model is greatly reduced compared to conventional approaches, where high-fidelity simulations are directly used to set up the model. At the same time, the modeling accuracy is not compromised. The proposed technique is demonstrated using three examples of antenna structures. Comparisons with conventional modeling based on high-fidelity data approximation, as well as applications for antenna design, are also discussed

Parametric macromodeling of lossy and dispersive multiconductor transmission lines

We propose an innovative parametric macromodeling technique for lossy and dispersive multiconductor transmission lines (MTLs) that can be used for interconnect modeling. It is based on a recently developed method for the analysis of lossy and dispersive MTLs extended by using the multivariate orthonormal vector fitting (MOVF) technique to build parametric macromodels in a rational form. They take into account design parameters, such as geometrical layout or substrate features, in addition to frequency. The presented technique is suited to generate state-space models and synthesize equivalent circuits, which can be easily embedded into conventional SPICE-like solvers. Parametric macromodels allow to perform design space exploration, design optimization, and sensitivity analysis efficiently. Numerical examples validate the proposed approach in both frequency and time domain

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Robust simulation methodology for surface-roughness loss in interconnect and package modelings

In multigigahertz integrated-circuit design, the extra energy loss caused by conductor surface roughness in metallic interconnects and packagings is more evident than ever before and demands explicit consideration for accurate prediction of signal integrity and energy consumption. Existing techniques based on analytical approximation, despite simple formulations, suffer from restrictive valid ranges, namely, either small or large roughness/frequencies. In this paper, we propose a robust and efficient numerical-simulation methodology applicable to evaluating general surface roughness, described by parameterized stochastic processes, across a wide frequency band. Traditional computation-intensive electromagnetic simulation is avoided via a tailored scalar-wave modeling to capture the power loss due to surface roughness. The spectral stochastic collocation method is applied to construct the complete statistical model. Comparisons with full wave simulation as well as existing methods in their respective valid ranges then verify the effectiveness of the proposed approach. © 2009 IEEE.published_or_final_versio

Wideband characterization of printed circuit board materials up to 50 GHz

A traveling-wave technique developed a few years ago in the Missouri S&T EMC Laboratory has been employed until now for characterization of PCB materials over a broad frequency range up to 30 GHz. This technique includes measuring S-parameters of the specially designed PCB test vehicles. An extension of the frequency range of printed circuit board laminate dielectric and copper foil characterization is an important problem. In this work, a new PCB test vehicle design for operating up to 50 GHz has been proposed. As the frequency range of measurements increases, the analysis of errors and uncertainties in measuring dielectric properties becomes increasingly important. Formulas for quantification of two major groups of errors, repeatability (manufacturing variability) and reproducibility (systematic) errors, in extracting dielectric constant (DK) and dissipation factor (DK) have been derived, and computations for a number of cases are presented. Conductor (copper foil) surface roughness of PCB interconnects is an important factor, which affects accuracy of DK and DF measurements. This work describes a new algorithm for semi-automatic characterization of copper foil profiles on optical or scanning electron microscopy (SEM) pictures of signal traces. The collected statistics of numerous copper foil roughness profiles allows for introducing a new metric for roughness characterization of PCB interconnects. This is an important step to refining the measured DK and DF parameters from roughness contributions. The collected foil profile data and its analysis allow for developing design curves , which could be used by SI engineers and electronics developers in their designs --Abstract, page iii

Wideband characterization of printed circuit board materials up to 50 GHz

A traveling-wave technique developed a few years ago in the Missouri S&T EMC Laboratory has been employed until now for characterization of PCB materials over a broad frequency range up to 30 GHz. This technique includes measuring S-parameters of the specially designed PCB test vehicles. An extension of the frequency range of printed circuit board laminate dielectric and copper foil characterization is an important problem. In this work, a new PCB test vehicle design for operating up to 50 GHz has been proposed. As the frequency range of measurements increases, the analysis of errors and uncertainties in measuring dielectric properties becomes increasingly important. Formulas for quantification of two major groups of errors, repeatability (manufacturing variability) and reproducibility (systematic) errors, in extracting dielectric constant (DK) and dissipation factor (DK) have been derived, and computations for a number of cases are presented. Conductor (copper foil) surface roughness of PCB interconnects is an important factor, which affects accuracy of DK and DF measurements. This work describes a new algorithm for semi-automatic characterization of copper foil profiles on optical or scanning electron microscopy (SEM) pictures of signal traces. The collected statistics of numerous copper foil roughness profiles allows for introducing a new metric for roughness characterization of PCB interconnects. This is an important step to refining the measured DK and DF parameters from roughness contributions. The collected foil profile data and its analysis allow for developing design curves , which could be used by SI engineers and electronics developers in their designs --Abstract, page iii

Efficient integral equation based algorithms for parasitic extraction of interconnects with smooth or rough surface

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 187-198).This thesis describes a few efficient parasitic extraction algorithms based on integral equation methods. It has two parts. Part one describes the algorithms used in FastImp, a program for accurate analysis of wide-band electromagnetic effects in very complicated geometries of conductors. The program is based on a recently developed surface integral formulation and a Pre-corrected FFT accelerated iterative method, but includes a new piecewise quadrature panel integration scheme, a new scaling and preconditioning technique as well as a generalized grid interpolation and projection strategy. Computational results are given on a variety of integrated circuit interconnect structures to demonstrate that FastImp is robust and can accurately analyze very complicated geometries of conductors. Part two describes an efficient Stochastic Integral Equation (SIE) Method for computing the mean value and variance of the capacitance of interconnects with random surface roughness in O(Nlog2Ì(N)) time. An ensemble average Green's function is used to account for the surface roughness. A second-order correction scheme is used to improve the accuracy. A sparsification technique based on the Hierarchical Matrix method is proposed to significantly reduce the computational cost. The SIE method avoids the time-consuming Monte Carlo simulations and the discretization of rough surfaces. Numerical experiments show that the results of the new method agree very well with those of Monte Carlo simulations.by Zhenhai Zhu.Ph.D