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REVIEW ARTICLE

AI for chips: A systematic review of artificial intelligence in the semiconductor lifecycle

Ignacio Chechile1* ,  Jan Akmal1
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1 Department of Energy and Mechanical Engineering, School of Engineering, Aalto University, Espoo, Uusimaa , Finland
IJAMD, 026340031 https://doi.org/10.36922/IJAMD026340031
Received: 20 August 2026 | Revised: 3 September 2026 | Accepted: 18 September 2026 | Published online: 28 September 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Artificial intelligence (AI) methods such as machine learning, deep learning, and large language models are increasingly used to design systems, including integrated circuits. This work provides a thorough understanding of AI adoption across the full semiconductor engineering life cycle by collecting and analyzing a corpus of peer-reviewed articles from a curated list of journals and conference proceedings. The results of this systematic review indicate active work on AI use before tapeout and depict a thinly connected lifecycle, where methods focus on specific stages and barely cross neighboring-stage boundaries. Additionally, we analyzed the geographical and industrial landscape of the global AI-for-Chips conversation and found a notable rise of China as a leader in AI research for semiconductor development, amid a general decline in contributions from other countries, with only a few exceptions. This study represents the first work bridging AI, end-to-end semiconductor engineering, and national contributions. The work benefits designers, practitioners, engineers, entrepreneurs, decision-makers, and policymakers by highlighting opportunities and challenges in using AI for chips to improve productivity and sustainability in the era of silicon dominance.

Graphical abstract
Keywords
Artificial intelligence
Semiconductors
Ontology
Systematic literature review
Challenges
Funding
None.
Conflict of interest
Jan Akmal serves as an Editorial Board Member of the journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Other authors declare they have no competing interests.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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