Resumen:
In the rapidly evolving semiconductor industry, the ability to efficiently process and analyze vast amounts of data is crucial for optimizing manufacturing processes and ensuring product quality. This paper explores the application of data analytics architectures and design practices in the measurement and testing of semiconductors. By leveraging data analytics, manufacturers can identify trends, optimize processes, and detect anomalies throughout the semiconductor lifecycle. The research bridges the gap between test engineers and data analytics, providing a novel perspective on integrating modern data-processing techniques into semiconductor testing. Key requirements for semiconductor measurement and testing are identified, and various data analytics architectures, including those based on Microsoft Azure, Oracle, and AWS, are analyzed for their suitability in addressing these needs. The study includes recommendations for adopting data analytics in semiconductor manufacturing, emphasizing the importance of tailored solutions, progressive development, and continuous monitoring to enhance efficiency and profitability. It concludes with a hypothetical example to illustrate the practical application of the proposed architectures and strategies.
