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Multilayered packages and boards, such as high performance server boards, contain thousands of signal lines, which have to be routed on and through several layers with power/ground planes in between. There can be noise coupling not only in the transversal direction through the power/ground planes in such a structure, but also vertically from one plane pair to another through the apertures and via holes. In addition, the continuous increase in power demand along with reduced Vdd values results in significant current requirement for the future chips. Hence, the parasitic effects of the power distribution system become increasingly more critical regarding the signal integrity and electromagnetic interference properties of cost-effective high-performance designs. We present a multilayer finite-difference method (M-FDM), which is capable of characterizing such noise coupling mechanisms. This method allows to consider realistic structures, which would be prohibitive to simulate using fullwave simulators.

A fast and accurate layout-level synthesis and optimization technique for embedded passive RF components and circuits such as inductors and bandpass filters have been presented. The filters are composed of embedded inductors and capacitors in a multilayer liquid crystalline polymer substrate. The proposed approach is based on a combination of segmented lumped-circuit modeling, nonlinear mapping using polynomial functions, artificial neural network-based methods, and circuit-level optimization. Synthesis and optimization results of inductors for spiral/loop designs based on microstrip and stripline configuration are within 5% of data obtained from electromagnetic (EM) simulations. For RF circuits, the methodology has been verified through synthesis of 2.4-and 5.5-GHz bandpass filters with and without transmission zeros. Scalability has been shown over a range of 2-3 and 4-6 GHz, respectively, with bandwidth variation of 0.5%-3% of center frequency. The synthesized models are within 3%-5% of EM simulation data.Index Terms-Artificial neural networks (ANNs), filter synthesis, inductor optimization, liquid crystalline polymer (LCP), synthesis.

In this paper a modeling methodology using spline functions with finite time difference is proposed for modeling digital U 0 drivers. Digital driver circuits can be accurately modeled using their static characteristics for normal excitations, but for faster excitations static characteristic models tend to lose their accuracy as the dynamic characteristics start to dominate the static characteristics. Spline function with finite time difference modeling includes previous time instances to capture dynamic characteristics for accurate modeling of digital drivers. In this paper the speed and accuracy of the proposed method is analyzed and compared with Radial Basis Function (RBF) modeling for different test cases.

In this paper, power supply noise is modeled accurately using eficient macro-models of non-linear digital drivers. Spline function with $&e time difference approximation modeling technique takes into account both the static and the dynamic memory characteristics of the driver during modeling. For power supply noise analysis, the above method has been atended to multiple ports by taking the previous time instances of the power supply voltage/current into account. The method discussed can be used to capture sensitive effects like Simultaneous Switching Noise (SSN) and cross talk accurately, when multiple drivers are switching simulta~ous(y. A comparison study between the presented method and the transistor level driver models indicate a computational speed-up in the range of 10-40 with an error of less than 5%. For highly non-linear drivers, a method based on Artificial Neural Networks (ANN) is briefly discussed to capture SSN.