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

Dominik Zupan; Nikolaus Czepl; Daniel Kircher

doi:10.1109/SMACD55068.2022.9816240

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

Institute of Electronics (4390)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-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 language	English
Title of host publication	2022 International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design (SMACD 2022)
Number of pages	4
ISBN (Electronic)	978-1-6654-6703-2
DOIs	https://doi.org/10.1109/SMACD55068.2022.9816240
Publication status	Published - Jul 2022
Event	18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design: SMACD 2022 - Villasimius, Italy Duration: 12 Jun 2022 → 15 Jun 2022

Conference

Conference	18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design
Abbreviated title	SMACD 2022
Country/Territory	Italy
City	Villasimius
Period	12/06/22 → 15/06/22

Keywords

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

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10.1109/SMACD55068.2022.9816240

Cite this

Framework for Developing Neural Network Regression Models Predicting the Influence of EMI on Integrated Circuits. / Zupan, Dominik ; Czepl, Nikolaus ; Kircher, Daniel.
2022 International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design (SMACD 2022). 2022.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Zupan, D , Czepl, N & Kircher, D 2022, Framework for Developing Neural Network Regression Models Predicting the Influence of EMI on Integrated Circuits. in 2022 International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design (SMACD 2022). 18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design, Villasimius, Italy, 12/06/22. https://doi.org/10.1109/SMACD55068.2022.9816240

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title = "Framework for Developing Neural Network Regression Models Predicting the Influence of EMI on Integrated Circuits",

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{\textquoteright}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.",

keywords = "electromagnetic interference, EMI induced offset, prediction, neural network, differential amplifier stage, artificial intelligence",

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N2 - 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.

AB - 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.

KW - electromagnetic interference

KW - EMI induced offset

KW - prediction

KW - neural network

KW - differential amplifier stage

KW - artificial intelligence

U2 - 10.1109/SMACD55068.2022.9816240

DO - 10.1109/SMACD55068.2022.9816240

M3 - Conference paper

BT - 2022 International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design (SMACD 2022)

T2 - 18th International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuit Design

Y2 - 12 June 2022 through 15 June 2022

ER -

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

Abstract

Conference

Keywords

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