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集中在开发通用方法上，但该项目侧重于开发专用方法，为挖掘测试测量数据进行了优化。该项目中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，为克服汽车芯片领域的挑战提供解决方案。此外，该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，从而对半导体行业产生更广泛的影响。在生产过程中，汽车芯片产品要经过一个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此过程中收集的所有数据，该过程包括多个阶段，从产品制造开始一直到电子系统中的评估。分析是指从数据中提取的知识的发现，解释和利用。该研究旨在实现有效和强大的分析，以提高产品质量并降低生产测试成本。为了提高效率，将设计新的软件工具和方法，以自动将领域知识纳入分析。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有利于半导体行业，促进大规模数据挖掘的有效使用，以优化测试过程。从科学的角度来看，这项研究将使人们更深入地了解测试数据分析的局限性，并使其能够稳健地实施。总体而言，该项目旨在开发下一代测试数据分析软件，从而将研究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.