AI-Driven Semiconductor Design: Accelerating Innovation in Next-Generation Computing Architectures

Solène Delecourt

AI-Driven Semiconductor Design: Accelerating Innovation in Next-Generation Computing Architectures

Author(s)	Solène Delecourt
Country	United States
Abstract	The semiconductor industry is entering an increasingly complex design era in which conventional engineering approaches are being complemented by artificial intelligence (AI), machine learning, generative AI, and automated optimisation techniques. As transistor scaling becomes more challenging and computing architectures diversify across CPUs, GPUs, AI accelerators, chiplets, edge processors, and application-specific integrated circuits, semiconductor design requires the exploration of increasingly large and complex design spaces. AI-driven semiconductor design provides new approaches for electronic design automation, logic synthesis, floorplanning, routing, power optimisation, verification, hardware–software co-design, and architecture exploration. This paper examines the role of AI throughout the semiconductor design lifecycle and analyses how intelligent algorithms can accelerate design-space exploration, improve performance, reduce power consumption, and shorten development cycles. A conceptual qualitative methodology is employed to examine the integration of AI with electronic design automation, semiconductor architectures, advanced packaging, and emerging computing paradigms. The paper proposes an integrated AI-driven semiconductor design framework connecting architectural exploration, automated design generation, physical implementation, verification, optimisation, and continuous learning. Particular attention is given to reinforcement learning, graph neural networks, generative AI, optimisation algorithms, chiplet architectures, heterogeneous computing, and hardware–software co-design. The study also identifies challenges related to data availability, model reliability, explainability, verification, intellectual property, cybersecurity, manufacturing constraints, and human oversight. The analysis suggests that AI is evolving from an optimisation tool into a potential design collaborator capable of assisting engineers across multiple stages of semiconductor development. The future of semiconductor innovation will increasingly depend on the interaction between human engineering expertise and AI-driven computational design.
Keywords	Semiconductor Design, Artificial Intelligence, Electronic Design Automation, Machine Learning, Chip Design, Generative AI, Chiplets, Hardware–Software Co-Design, Computing Architectures, Design Automation.
Field	Engineering
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-07-07

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Driven Semiconductor Design: Accelerating Innovation in Next-Generation Computing Architectures

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