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Integration of AI and Machine Learning in Semiconductor Manufacturing for Defect Detection and Yield Improvement

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  • Monish Katari

  • Lavanya Shanmugam

  • Jesu Narkarunai Arasu Malaiyappan

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into semiconductor manufacturing has revolutionized defect detection and yield improvement processes. AI and ML algorithms analyze vast amounts of data generated during fabrication to enhance quality control, reduce defects, and optimize production yields. This paper provides an overview of AI and ML applications in semiconductor manufacturing, focusing on their roles in defect detection methodologies, process optimization, and yield enhancement strategies. Case studies and current advancements illustrate the transformative impact of AI and ML technologies on semiconductor fabrication, highlighting their potential to drive future advancements in microelectronics.

Suggested Citation

  • Monish Katari & Lavanya Shanmugam & Jesu Narkarunai Arasu Malaiyappan, 2024. "Integration of AI and Machine Learning in Semiconductor Manufacturing for Defect Detection and Yield Improvement," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 3(1), pages 418-431.
    • Handle: RePEc:das:njaigs:v:3:y:2024:i:1:p:418-431:id:191
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    References listed on IDEAS

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    1. Chandrashekar Althati & Manish Tomar & Lavanya Shanmugam, 2024. "Enhancing Data Integration and Management: The Role of AI and Machine Learning in Modern Data Platforms," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 2(1), pages 220-232.
    2. Chandrashekar Althati & Manish Tomar & Jesu Narkarunai Arasu Malaiyappan, 2024. "Scalable Machine Learning Solutions for Heterogeneous Data in Distributed Data Platform," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 4(1), pages 299-309.
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