Abstract
This article demonstrates that causal discovery approaches can be applied to analog electronic circuits made of Bipolar Junction Transistors (BJTs) to find out the causal relationships among different variables of the circuit. Moreover, the obtained causal relationship structure in the form of a Directed Acyclic Graph (DAG), can be used for diagnosis and analysis of such circuits. First, it is shown that the operation process of a transistor has an inherent notion of causality, which is then exploited to show that the various parameters of a BJT circuit can be expressed in the form of Structural Equation Models (SEM). The results demonstrated using data generated using LTspice establishes that the causal structure of a BJT circuit can be retrieved using data driven causal discovery algorithms. This opens new horizons for analysis and diagnosis of BJT circuits. An example case study of circuit diagnosis is presented to showcase the capability and efficiency of the proposed method.
Original language | English (US) |
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Title of host publication | 2023 62nd IEEE Conference on Decision and Control, CDC 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 8223-8228 |
Number of pages | 6 |
ISBN (Electronic) | 9798350301243 |
DOIs | |
State | Published - 2023 |
Event | 62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: Dec 13 2023 → Dec 15 2023 |
Publication series
Name | Proceedings of the IEEE Conference on Decision and Control |
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ISSN (Print) | 0743-1546 |
ISSN (Electronic) | 2576-2370 |
Conference
Conference | 62nd IEEE Conference on Decision and Control, CDC 2023 |
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Country/Territory | Singapore |
City | Singapore |
Period | 12/13/23 → 12/15/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.