SHF: Small: Collaborative Research: Correlation Mining and its Applications in Test Cost Reduction, Yield Enhancement, and Performance Calibration in Analog/RF Circuits

该项目旨在提高模拟/射频(RF)集成电路(IC)的质量和可靠性，方法是开发一种智能系统，系统地探索在其生产生命周期中产生的丰富信息，并将其应用于提高其设计、制造和测试的有效性。虽然大量的数据是通过对实际制造的电路进行广泛的设计仿真和测量而获得的，但目前还缺乏有效地从这些数据中提取有意义的信息的正式方法。将通过该项目开展的研究活动旨在通过根据机器学习和数据挖掘领域的最新发展开发相关性挖掘方法来填补这一空白。最终，使用工业合作伙伴(即IBM和德州仪器)提供的实际IC生产数据，该项目的目标是展示这种相关性对降低测试成本、提高生产成品率和实现模拟/射频电路的制造后校准所产生的影响。该项目将促进以成本效益方式实现坚固耐用的电子电路和系统，从而实现更可靠的计算和促进技术的可信性。拟议的研究还得到了教育和外联活动的补充，包括开发一门新的关于机器学习在计算机辅助设计和测试中的应用的研究生级课程，并让研究生、本科生和高中生与首席调查员小组、工业伙伴和国际合作者的研究实验室一起参与研究。

This project seeks to improve the quality and reliability of Analog/Radio-Frequency (RF) integrated electronic circuits (ICs) by developing an intelligent system for systematically exploring the wealth of information generated throughout their production lifetime and applying it towards improving the effectiveness of their design, manufacturing, and testing. While a large amount of data is made available through extensive design simulations and measurements on actual fabricated circuits, there currently exists a striking lack of formal methods to efficiently extract meaningful information from this data. The research activities that will be carried out through this project aim to fill this void by developing correlation mining methods based on the most recent developments in the fields of machine learning and data mining. Ultimately, using data from actual IC productions provided by industrial partners (i.e. IBM and Texas Instruments), the objective of this project is to demonstrate the impact that such correlations can have on reducing the cost of testing, enhancing the yield of the production and enabling post-manufacturing calibration of analog/RF circuits. This project will facilitate the cost-effective realization of robust electronic circuits and systems, thus enabling more reliable computing and promoting technology trustworthiness. The proposed research is complemented by educational and outreach activities, including the development of a new graduate-level course on applications of Machine-Learning in Computer Aided Design and Test and the involvement of graduate, undergraduate and high-school students in research with the groups of the Principal Investigators, the industrial partners, and the research laboratory of the international collaborator.