SHF: Small: End-To-End Test Data Analytics For Automotive Chip Production Lines

该项目以汽车行业为研究驱动力，旨在开发新的数据挖掘解决方案，通过提高测试效率和降低测试成本来应对可靠的低功耗、高质量芯片生产的挑战。虽然数据挖掘社区的研究更多地关注于开发通用方法，但该项目专注于开发专门的方法，针对挖掘测试测量数据进行优化。该项目开发的技术可以补充现有的数据挖掘研究，并带来协同效应。通过该项目开发的技术将转移到行业中，为克服汽车芯片行业的挑战提供解决方案。此外，研究将与教育活动相结合，以制作出版物、课程材料、教程和软件工具，对半导体行业产生更广泛的影响。在生产中，汽车芯片产品要经过相当全面的测试过程，以确保其质量。端到端测试数据是指在这个过程中收集的所有数据，包括从产品制造到电子系统中的评估的多个阶段。分析是指发现、解释和利用从数据中提取的知识。这项研究的目的是实现有效和稳健的分析，以提高产品质量，降低生产测试成本。为提高效率，将设计新的软件工具和方法，以便在分析中自动纳入领域知识。为了稳健性，将开发新的方法来确定数据挖掘结果的意义。从实践的角度来看，通过研究开发的解决方案将通过促进大规模数据挖掘的有效使用来优化测试过程，从而使半导体行业受益。从科学的角度来看，这项研究将提供对测试数据分析的局限性的更深入的理解，并使其能够健壮地实施。总体而言，该项目旨在开发下一代测试数据分析软件，从而使研究能够应用于半导体芯片生产测试环境中遇到的各种场景。

Using automotive industry as the research driver, this project aims to develop novel data mining solutions to address the challenge of reliable low-power, high quality chip production by improving the effectiveness and reducing the cost of testing them. While research in the data mining community focuses more on developing generic approaches, this project focuses on developing dedicated approaches, optimized for mining test measurement data. The techniques developed in this project can complement and bring synergies to existing data mining research. The technologies developed through this project will be transferred to the industry, providing solutions to overcome the challenges in the automotive chip sector. Furthermore, the research will be integrated with educational activities to produce publications, curriculum materials, tutorials, and software tools for broader impacts to the semiconductor industry. In production, automotive chip products go through a rather comprehensive test process to assure their quality. End-to-end test data refers to all data collected in this process which comprises multiple stages, starting from product manufacturing all the way to evaluation in an electronic system. Analytics refers to the discovery, interpretation, and utilization of knowledge extracted from the data. This research aims to enable effective and robust analytics in order to improve product quality and reduce production test cost. For effectiveness, novel software tools and methodologies will be designed to automatically incorporate domain knowledge in the analytics. For robustness, new approaches will be developed to determine the meaningfulness of data mining results. From a practical perspective, solutions developed through the research will benefit the semiconductor industry by facilitating the effective use of large-scale data mining for test process optimization. From a scientific perspective, this research will provide a deeper understanding of the limitations with test data analytics and enable its robust implementation. Overall, this project aims to develop the next-generation test data analytics software, thereby enabling application of the research to diverse scenarios encountered in semiconductor chip production test environments.