REVOLUTIONIZING SEMICONDUCTOR DESIGN AND MANUFACTURING WITH AI

Authors

  • Prashis Raghuweanshi IEEE Member Author

DOI:

https://doi.org/10.60087/jklst.vol3.n3.p.272-277

Abstract

The semiconductor industry plays a vital role in driving technological advancements, and the incorporation of AI (Artificial Intelligence) can greatly enhance its efficiency and productivity. Through optimizing material usage and reducing defects, AI can significantly reduce costs and enhance production efficiency and product quality. However, despite the increasing interest in AI applications in the semiconductor industry, comprehensive reviews are lacking to systematically analyze existing research and identify the challenges and opportunities in this field. This review aims to bridge this gap by providing a thorough overview of AI-driven techniques in optimizing semiconductor manufacturing and offering valuable insights for future research directions. The integration of Artificial Intelligence (AI) into chip design marks a transformative phase for the semiconductor industry. Traditional design methodologies, often labor-intensive and time-consuming, are increasingly constrained by human expertise and iterative processes. Generative AI, utilizing advanced machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offers innovative approaches to automate and optimize various stages of chip design. This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation. Through case studies, we demonstrate significant improvements in design efficiency, performance optimization, and reduced time-to-market. Additionally, we address challenges such as data availability, model interpretability, and the integration of AI-generated designs into existing verification workflows. The findings highlight the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.

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References

Kurzgesagt. (2017, December 8). Quantum Computers Explained - Limits of Human Technology. YouTube. Retrieved from https://www.youtube.com/watch?v=limit-human-tech

Semiconductor Engineering. Quantum Effects at 7/5nm and Beyond. Retrieved from https://semiconductor-eng.com/quantum-effects-7-5nm-beyond

Kern, E.-M. (2016). Verteilte Produktentwicklung. In U. Lindemann (Ed.), Handbuch Produktentwicklung (pp. 455-481). Hanser München.

Fortune Business Insights. (2021). The global semiconductor market is projected to grow from $452.25 billion in 2021 to $803.15 billion in 2028 at a CAGR of 8.6% in forecast period 2021-2028. Retrieved from https://www.fortunebusinessinsights.com/semiconductor-market-102365

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Published

22-09-2024

How to Cite

Raghuweanshi, P. . (2024). REVOLUTIONIZING SEMICONDUCTOR DESIGN AND MANUFACTURING WITH AI. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 272-277. https://doi.org/10.60087/jklst.vol3.n3.p.272-277