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

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.

Keywords

Artificial intelligence / Semiconductors / Ontology / Systematic literature review / Challenges

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