Analog integrated circuit design with machine learning

Sirui Gao

doi:10.54254/2753-8818/5/20230495

Research Article

Open access

Published on 25 May 2023

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Gao,S. (2023). Analog integrated circuit design with machine learning. Theoretical and Natural Science,5,786-793.

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Analog integrated circuit design with machine learning

Sirui Gao *^,1,

¹ Western University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/5/20230495

Abstract

Due to the widespread application of semiconductor technology in integrated circuits, more and more design studies on analog integrated circuits are gradually being implemented. However, due to the nature of analog integrated circuits, it is time-consuming and inefficient. Therefore, there are lots of experts studying how to reduce the design cycle of analog ICs. The use of machine learning in analog circuits stands out, as machine learning-based design methods have significantly reduced the analog cycle time. This review report will first introduce the algorithms related to machine learning, and the second half will outline the existing applications of machine learning in an analog integrated circuit and compare them.

Keywords

machine learning, analog circuit design, artificial intelligence, optimization

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Cite this article

Gao,S. (2023). Analog integrated circuit design with machine learning. Theoretical and Natural Science,5,786-793.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)

Conference website: https://www.confciap.org/

ISBN：978-1-915371-53-9(Print) / 978-1-915371-54-6(Online)

Conference date: 25 March 2023

Editor：Marwan Omar, Roman Bauer

Series: Theoretical and Natural Science

Volume number: Vol.5

ISSN：2753-8818(Print) / 2753-8826(Online)

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