
Artificial intelligence in analogue circuit design
- 1 University of Warwick
* Author to whom correspondence should be addressed.
Abstract
Analog circuit design plays an integral part in modern society; however, traditional approaches often fall short in terms of efficiency and effectiveness. This paper explores the application and challenges presented by artificial intelligence (AI) technologies in analog circuit design. The objective of this paper was to evaluate the capacity of artificial intelligence technologies such as machine learning, deep learning and reinforcement learning to advance circuit modeling, optimization and layout design processes. A comprehensive literature review and analysis were performed. The study finds that while AI can make significant contributions to circuit design, several challenges must first be overcome. These include data scarcity for training AI models and the opaque nature of AI decisions which prevent their widespread acceptance. Finally, future research directions such as creating synthetic data or developing more interpretable AI models were identified as potential future research avenues.
Keywords
Artificial Intelligence (AI), Analog Circuit Design, Machine Learning (ML), Deep Learning (DL), Circuit Modeling
[1]. McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI magazine, 27(4), 12-14.
[2]. Mitchell, T. (1997). Machine Learning. McGraw Hill.
[3]. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
[4]. W. B. Dunbar, “Artificial intelligence and analog circuit design: two case studies,” Analog Integrated Circuits and Signal Processing, vol. 14, no. 3, pp. 279–296, 1997.
[5]. Z. Zhang and X. Li, “Machine Learning for High-Speed Analog Circuit Simulation and Optimization,” Proceedings of the 45th Annual Design Automation Conference on - DAC ’08, 2008.
[6]. N. Liu, Z. Li, J. Han, and L. Pileggi, “Reinforcement Learning for Analog Circuit Design,” Proceedings of the 56th Annual Design Automation Conference 2019 - DAC ’19, 2019.
[7]. A. Z. Kahng, J. Lienig, I. L. Markov, and J. Hu, “VLSI Physical Design: From Graph Partitioning to Timing Closure,” Springer, 2011.
[8]. Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2022). Challenges in Deploying Machine Learning: a Survey of Case Studies. ACM Comput. Surv., 1(1), Article 1.
[9]. Preece, A., 2018. Asking 'Why' in AI: Explainability of intelligent systems - perspectives and challenges. Intell Sys Acc Fin Mgmt, 25(2), pp.63-71.
Cite this article
Chen,A. (2024). Artificial intelligence in analogue circuit design. Applied and Computational Engineering,48,181-185.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).