Framework for Developing Neural Network Regression Models Predicting the Influence of EMI on Integrated Circuits

Dominik Zupan*, Nikolaus Czepl, Daniel Kircher

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

In this paper we present a framework to develop regression models that predict the behaviour of integrated circuits (ICs) when exposed to electromagnetic interference (EMI). To do so, we use techniques that are commonly used in artificial intelligence (AI) applications. We show, how to create data sets from simulation, how to split these into training and test data, and how to pre-process these. Further on, we explain which AI models are suited best for predicting changes due to EMI. We also elaborate the structure and complexity of these models with regard to the model’s capability to fit the training and test data. We implement this framework using the Tensorflow library in Python and explain its application on an example predicting the EMI induced offset voltage. Therefore, we provide the data sets as well as the source code of the framework.
Original languageEnglish
Title of host publication2022 International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design (SMACD 2022)
Publication statusSubmitted - 4 Mar 2022
Event18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design: SMACD 2022 - Villasimius, Italy
Duration: 12 Jun 202215 Jun 2022

Conference

Conference18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design
Abbreviated titleSMACD 2022
Country/TerritoryItaly
CityVillasimius
Period12/06/2215/06/22

Keywords

  • electromagnetic interference
  • EMI induced offset
  • prediction
  • neural network
  • differential amplifier stage
  • artificial intelligence

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