Bert Simonovich

Bert Simonovich


Lambert (Bert) Simonovich graduated from Mohawk College of Applied Arts and Technology, Hamilton, Ontario Canada, as an Electronic Engineering Technologist. Over a 32-year career, working at Bell Northern Research/Nortel in Ottawa Canada, he helped pioneer several advanced technology solutions into products. He has held a variety of engineering, research and development positions, eventually specializing in high-speed signal integrity and backplane design. After leaving Nortel in 2009, he founded Lamsim Enterprises Inc., where he continues to provide innovative signal integrity and backplane solutions as a consultant. He has authored several publications and holder of two US patents. In addition to being a senior member of IEEE, he currently serves as a member of DesignCon's Technical Program Committee, EDICon's Technical Advisory Committee and Signal Integrity Journal's Editorial Advisory Board. His current research interests include high-speed signal integrity, modeling and characterization of high-speed serial link architectures. His most notable modeling achievement is the development of the "Cannonball-Huray" conductor roughness model used in several electronic design automation (EDA) software tools. 

ARTICLES

Eye Diagram

Practical Method for Modeling Conductor Surface Roughness Using Close Packing of Equal Spheres

Presented at DesignCon 2015

In the GB/s regime, accurate modeling of conductor losses is a precursor to successful high-speed serial link designs. In this paper, a practical method for modeling conductor surface roughness is presented. Obtaining the roughness parameters solely from manufacturers’ data sheets, conductor loss can now be accurately predicted from first principles. By using a close packing of equal spheres model, the radius of the spheres and area of the multi-sphere tiled base are determined then applied to the Huray “snowball” model. A case study, based on Megtron-6 and N4000-13EP dielectric with HVLP and VLP copper foils respectively, validates the model’s accuracy.


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