AI-Powered Revolution

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AI-Powered Revolution
Title:
AI-Powered Revolution
Journal Title:
IEEE Nanotechnology Magazine
Publication Date:
03 March 2025
Citation:
Kong, Z. H., James, A., Ata, S. K., Aung, K. M. M., Foo, C. S., James, A., & Yeo, K. S. (2025). AI-Powered Revolution. IEEE Nanotechnology Magazine, 19(2), 38–47. https://doi.org/10.1109/mnano.2025.3533936
Abstract:
The infusion of Artificial Intelligence (AI) within the intricate realm of analog circuits presents a transformative opportunity. By seamlessly blending the precision of AI algorithms with the nuanced operation of analog components, the landscape of analog circuit design has undergone a revolutionary transformation, enabling dynamic adaptation, fine optimization, and intelligent assimilation of insights gleaned from past data. Nevertheless, analog circuits face stringent functional and technological constraints, leading to a scarcity of data for modelling, and additional data acquisition entails costly and time-consuming simulations. This article introduces novel methodologies designed to enhance simulation efficiency and reduce associated costs, thereby enabling efficient and effective AI-driven analog circuit design. By leveraging data-driven AI approaches, the focus is on exploration of promising and feasible circuit design regions, improving AI model accuracy, and substantially mitigating the reliance on extensive simulations and significant manual effort. The results demonstrate a marked advancement in analog circuit design, showcasing how data-centric AI approaches can refine the design process, making it more efficient and cost-effective. This work is poised to set the stage for future developments where analog circuit design can be conducted with greater precision and efficiency.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Advanced Manufacturing and Engineering (AME) Programmatic Funds
Grant Reference no. : A19E3b0099

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Advanced Manufacturing and Engineering (AME) Programmatic Funds
Grant Reference no. : A20H6b0151
Description:
© 2025 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1932-4510
1942-7808
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