A review of quality control and process optimization in high-volume semiconductor manufacturing
1 Intel Corporation, Rio-Rancho New Mexico, USA.
2 Independent Researcher, Texas, USA.
3 Independent Researcher, USA.
Review
World Journal of Engineering and Technology Research, 2024, 03(02), 022–027.
Article DOI: 10.53346/wjetr.2024.3.2.0060
Publication history:
Received on 09 October 2024; revised on 19 November 2024; accepted on 22 November 2024
Abstract:
In the semiconductor industry, quality control (QC) and process optimization play crucial roles in sustaining high production standards and meeting the intense demands of global markets. As semiconductors become more essential in applications ranging from consumer electronics to high-stakes industries such as automotive and telecommunications, the need for stringent QC has increased. This review explores the methods used to enhance QC and optimize processes in high-volume semiconductor manufacturing. Traditional methods like Six Sigma and Statistical Process Control (SPC) are discussed alongside recent developments in automation and AI-driven optimization techniques. These advancements aim to improve defect detection, yield rates, and operational efficiency. This paper synthesizes findings from the latest research, highlighting key improvements in QC methods while acknowledging the limitations of current approaches. The review also proposes future research avenues, focusing on the integration of adaptive AI models and data governance practices to meet evolving industry challenges and regulatory requirements.
Keywords:
Quality Control; Semiconductor Manufacturing; Process Optimization; Six Sigma; Statistical Process Control; Artificial Intelligence; Machine Learning; Predictive Maintenance; Automation
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0