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 |
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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 | |
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 |
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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