A review of quality control and process optimization in high-volume semiconductor manufacturing

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.