PREDICTIVE MAINTENANCE IN SEMICONDUCTOR SYSTEMS: INSIGHTS FROM MACHINE INTELLIGENCE AND DATA-DRIVEN METHODS

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Year : 2026 | Volume : 13 | 01 | Page :
    By

    Shubhii Shukla,

  1. Assistant Professor, College of Management, IIMT Group of Colleges, Greater Noida, Uttar Pradesh, India

Abstract

With the fast-paced development of semiconductor technology comes the need to focus on device reliability, or how long devices will function and the likelihood of devices having operational issues. Predicting failures and avoiding downtime with the implementation of timely, actionable, and data-driven maintenance strategies are essential to insure devices function sustainably within predetermined performance levels. The implementation of predictive maintenance within artificial intelligence and machine learning technologies will provide the maintenance and repair tasks the needed focus shift from post-repair maintenance to proactive levels of management. In this study, I review the literature on predictive maintenance, focusing on new developments in machine intelligence and how they are incorporated into frameworks for predictive maintenance in the semiconductor industry. In addition to learning-based predictive models used in semiconductor manufacture and operation, I assess and compare predictive maintenance solutions, paying particular attention to important performance metrics including prediction accuracy, system reliability, and operational and geographic scalability. This assessment also looks at recent studies on reliability improvement and predictive maintenance tailored to semiconductor devices, pointing out areas where the field lacks attention and uptake. In order to provide insight into areas that need more research and development, the talk is structured around the technological difficulties, data constraints, and implementation restrictions that predictive maintenance systems for semiconductors now face. In this paper, I survey the research within predictive maintenance, particularly the cutting-edge machine intelligence working with the research of predictive maintenance and its applications within the semiconductors sector. I gauge and benchmark predictive maintenance technologies alongside learning algorithm predictive modeling within semiconductors for parameters of accuracy, reliability, and geographic and operational scalability. Lastly, this survey details research within predictive maintenance, and reliability enhancement research specific to semiconductors about the gap of focus within the field. It’s framed around the challenges and limitations predictive semiconductors maintenance currently faces within its implementation.

Keywords: Semiconductor Failure, Machine Learning Applications, Predictive Maintenance, Decision Making, Predictive Model

How to cite this article:
Shubhii Shukla. PREDICTIVE MAINTENANCE IN SEMICONDUCTOR SYSTEMS: INSIGHTS FROM MACHINE INTELLIGENCE AND DATA-DRIVEN METHODS. Journal of Semiconductor Devices and Circuits. 2026; 13(01):-.
How to cite this URL:
Shubhii Shukla. PREDICTIVE MAINTENANCE IN SEMICONDUCTOR SYSTEMS: INSIGHTS FROM MACHINE INTELLIGENCE AND DATA-DRIVEN METHODS. Journal of Semiconductor Devices and Circuits. 2026; 13(01):-. Available from: https://journals.stmjournals.com/josdc/article=2026/view=238975


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Ahead of Print Subscription Review Article
Volume 13
01
Received 17/12/2025
Accepted 02/02/2026
Published 20/03/2026
Publication Time 93 Days
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