SI/PI-Database of PCB-Based Interconnects for Machine Learning Applications

Abstract

A database is presented that allows the investigation of machine learning (ML) tools and techniques in the signal integrity (SI), power integrity (PI), and electromagnetic compatibility (EMC) domains. The database contains different types of printed circuit board (PCB)-based interconnects and corresponding frequency domain data from a physics-based (PB) tool and represent multiple electromagnetic (EM) aspects to SI and PI optimization. The interconnects have been used in the past by the authors to investigate ML techniques in SI and PI. However, many more tools and techniques can be developed and applied to these structures. The setup of the database, its data sets, and examples on how to apply ML techniques to the data will be discussed in detail. Overall 78961 variations of interconnects are presented. By making this database available we invite other researchers to apply and customize their ML techniques using our results. This provides the possibility to accelerate ML research in EMC engineering without the need to generate expensive data.

Keywords

• Artificial neural network
• database
• electromagnetic compatibility
• machine learning
• power integrity
• signal integrity