IISc ECE Webinar :- System-level EMI/EMC Simulation – how machine learning can help

Описание: Dipanjan Gope Ph.D. is an Associate Professor in Electrical Communication Engineering at the Indian Institute of Science, Bangalore. He is also co-founder and CEO at Simyog Technology Pvt. Ltd. a spin-off from IISc focused on Design and Sign-off tools for Automotive Electronics. His research interests lie in the areas of Computational Electromagnetics and Electronic Design Automation. Dr. Gope is a founding member at Nimbic (acquired by Mentor Graphics) a spin-off from the University of Washington, Seattle. He received his Ph.D. and M.S. degrees in Electrical Engineering from the University of Washington, Seattle, and BTech in Electronics and Electrical Communication Engineering from the Indian Institute of Technology, Kharagpur.

Abstract:
EMI/EMC failures contribute to around 30% of the failures in the verification stage costing the electronics industry billions in lost revenue. Today, in general, EMI/EMC compliance is met by preparing a prototype and performing measurements in a standard laboratory. If the Equipment-Under-Test (EUT) fails compliance, the engineer has to diagnose the problem, modify the design, and re-prototype. This leads to delay in time-to-market and canceled projects. EMI/EMC system simulation is used rarely unlike in Signal Integrity/Power Integrity problems where simulation is predominant. The reason can be attributed to: (a) the time and memory complexity of a full-system simulation and (b) the lack of system-level models in particular for ICs. In this presentation, a model-based system-level simulation approach for EMI/EMC will be discussed. Models of different components of an EMI/EMC laboratory are generated and fed into a hybrid 3D and 2D electromagnetic simulation framework. Examples of models include: (a) IC models in the form of ICIM (IEC-62433-4) and ICEM-CE (IEC-62433-2) and ICEM-RE (IEC-62433-3) and (b) proposed ECU and off-ECU models. New research in generation of operating condition dependent models will be discussed.