International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AWS Data Lakes, Machine Learning, and AI-Driven Insights for Efficiency, Quality, and Innovation Transforming Semiconductor Manufacturing

Author(s) Padmaja Pulivarthy
Country United States
Abstract Driving everything from smartphones and autonomous cars to artificial intelligence and high-performance computing systems, the semiconductor industry is pillar of modern technology. Still, the sector is suffering increasing pains in the form of increased chip design complexity, better product quality, faster time-to--market, and more demand for reasonably priced manufacturing. In the middle of these challenges, old models of manufacturing—marked by isolated processes, fragmented data systems, and rigid control mechanisms—are inadequate. This article explores how the convergence of Amazon Web Services (AWS) Data Lakes, Machine Learning (ML), and Artificial Intelligence (AI) is enabling a radical change of semiconductor manufacturing into a scalable, smart, and efficient solution for these industry concerns.
By combining massive volumes of structured and unstructured data from all over the semiconductor production lifeline, AWS Data Lakes represent the backbone of this transformation. These cover sensors, equipment logs, manufacturing execution systems (MES), and enterprise resource planning (ERP) systems. Manufacturers can automate data intake, preparation, and cataloguing using Amazon S3, AWS Glue, and Lake Formation while preserving governance, security, and access. Driven by this shared data architecture, real-time analytics enable open processes and dynamic decision-making.
Leading semiconductor companies including TSMC and Intel case studies show the practical benefits of AWS-driven digital transformation. Among the examples are lowered unplanned downtime of up to 40%, yield increases of at least 6%, and shortened throughput times. Globally coordinated across fab sites with consistent, compliant, and rapid responsiveness to changing market needs, AWS infrastructure flexibility also enables
Although the promise is great, the road to semiconductor production driven by artificial intelligence is not an easy one. Important problems still are data interoperability, intellectual property (IP) protection, cybersecurity, and lack of AI/ML knowledge. Still, strategically used AWS Data Lakes, ML, and AI technologies are starting to be a main enabler for intelligent, resilient, and innovative semiconductor ecosystems.
This paper presents real-world applications, case studies, and strategic viewpoints on how these technologies are changing semiconductor production, therefore offering a road map for operational excellence and long-term competitive advantage in the digital age.
Field Engineering
Published In Volume 4, Issue 6, November-December 2022
Published On 2022-12-09
DOI https://doi.org/10.36948/ijfmr.2022.v04i06.47401
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E-ISSN 2582-2160
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