Author :
Publisher :
ISBN 13 :
Total Pages : 57 pages
Book Rating : 4.:/5 (887 download)
Book Synopsis Passive Macromodeling Methodology for High-speed Interconnects by :
Download or read book Passive Macromodeling Methodology for High-speed Interconnects written by and published by . This book was released on 2014 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: A crucial element in any physical, electronic system is the interconnects, which are responsible for the power delivery and signal transmission, from the circuitry within an integrated chip to the printed circuit board (PCB) interconnection network. The demand of digital systems to provide gigabit data rates has brought about engineering challenges related to reliably convey high speed signals within the chip, and sending these signals beyond the integrated circuit (IC) packaging. Macromodeling is a methodology employed with the goal to perform time-domain SPICE analysis of these interconnects, using their frequency transfer characteristics to extract a SPICE equivalent circuit, in order to predict and mitigate their noise performance behavior with the goal of improving signal transmission. Generally, time-domain SPICE simulations are commonly used for electromagnetic compatibility (EMC) and signal integrity (SI) analysis of interconnects. The accuracy of such analysis depends on the macromodels used for emulating the frequency transfer characteristic of the interconnect. These models should be broadband and preserve the physical properties of the materials, such as causality and passivity. The passivity constraint associated with macromodeling is one of the more challenging requirements to satisfy, which is a guarantee of the positive realness of the interconnect model across all frequencies, or that there is no energy gain performed by the model. This effort proposes a method for the analysis of single-input single-output macromodels, implementing the use of non-negative least squares fitting. Furthermore, multi-port macromodel analyses are demonstrated, and show good agreement between model and data, while achieving model-order reduction, and satisfying the passivity and causality requirements for the respective macromodel.