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

SHF：小型：协作研究：相关性挖掘及其在模拟/射频电路测试成本降低、良率提高和性能校准中的应用

• 批准号: 0916415
• 负责人: Petros Drineas
• 金额: $ 21万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916415&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Correlation

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.

该项目旨在提高模拟/射频（RF）集成电子电路（ic）的质量和可靠性，通过开发一个智能系统，系统地探索其生产生命周期中产生的大量信息，并将其应用于提高其设计、制造和测试的有效性。虽然通过广泛的设计模拟和实际制造电路的测量可以获得大量数据，但目前明显缺乏从这些数据中有效提取有意义信息的正式方法。通过该项目开展的研究活动旨在通过开发基于机器学习和数据挖掘领域最新发展的相关挖掘方法来填补这一空白。最终，利用工业合作伙伴（即IBM和德州仪器）提供的实际IC生产数据，该项目的目标是证明这种相关性对降低测试成本、提高生产产量和实现模拟/射频电路的制造后校准的影响。该项目将促进稳健电子电路和系统的成本效益实现，从而实现更可靠的计算和提高技术可信度。拟议的研究还将辅以教育和外展活动，包括开发一门新的研究生水平的课程，介绍机器学习在计算机辅助设计和测试中的应用，以及让研究生、本科生和高中生与主要研究人员、工业合作伙伴和国际合作伙伴的研究实验室一起参与研究。