Collaborative Research: SHF: Medium: Data-Efficient Uncovering of Rare Design Failures for Reliability-Critical Circuits

合作研究：SHF：中：以数据效率揭示可靠性关键电路的罕见设计故障

• 批准号: 2215573
• 负责人: Xiaoning Qian
• 金额: $ 56.7万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215573&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Data

While the proliferation of electronics has been driven by computing and consumer applications for a long time, integrated circuits (ICs) presently undergo accelerated integration into healthcare, transportation, robotics, and autonomous systems. In addition to provision of prescribed functionalities of sensing, computing, and processing, these ICs must meet stringent reliability specifications in order to safeguard performance and safety of the whole mission-critical system where deployed. Circuits designed to be fail-safe by design exhibit low occurrences of failure. However, having a sign of no failure under typical verification and test procedures yields no guarantee for meeting a given near-zero or extremely-low failure specification. On the other hand, exhaustiveness may never be achieved by brute-force failure detection, which results in an unacceptably high cost in simulation and testing. This project will develop efficient machine-learning techniques for extremely-rare circuit-failure detection without needing large amounts of expensive simulation or test data. The proposed techniques will enable cost-effective verification and test of reliability-critical ICs and mission-critical systems in general. The research undertaken will also enable the two groups at UC Santa Barbara and UT Dallas to educate and train undergraduate and graduate students, including women and underrepresented groups, thus expanding the and contributing to the much needed US technological workforce. It is believed that extracting critical failure information via machine learning within practical limits of available measurement or simulation data can go a long way towards extremely rare failure detection. This project centers on developing an active-learning framework that intelligently samples in the high-dimensional space of complex interacting design parameters, manufacturing variations, and operating conditions, achieving the goal of data-efficient detection of rare circuit failures. The targeted active-learning framework will be supported by the development of machine-learning model foundations and robust learning methods that can scale to high-dimensional parameter spaces. The key objective of this project is to make extremely-rare failure discovery and identification of the underlying failure mechanisms practically viable by extracting the maximum amount of useful information possible from a small amount of available data. The proposed extremely-rare failure discovery work will be broadly applicable to verification and failure analysis of analog, mixed-signal, radio-frequency, and memory circuits with stringent failure specifications and many other types of mission-critical systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

虽然很长一段时间以来，电子设备的扩散是由计算和消费者应用驱动的，但目前正在加速进入医疗保健，运输，机器人技术和自治系统的综合电路（ICS）。除了提供传感，计算和处理的规定功能外，这些IC还必须符合严格的可靠性规范，以维护部署的整个关键任务系统的绩效和安全性。设计为故障安全的电路表现出较低的故障出现。但是，在典型验证和测试程序中没有故障的迹象不能保证满足给定的接近零或极低的失败规范。另一方面，蛮力失败的检测永远无法实现，这导致模拟和测试的成本高昂。该项目将开发有效的机器学习技术，用于极度稀有的电路检测，而无需大量昂贵的模拟或测试数据。所提出的技术将启用具有成本效益的验证，并总体上对关键性IC和关键任务系统的测试。 进行的研究还将使圣塔芭芭拉分校和乌特·达拉斯分校的两个小组能够教育和培训本科生和研究生，包括妇女和代表性不足的群体，从而扩大并为美国急需的美国技术劳动力做出了贡献。据信，在可用测量的实际限制内通过机器学习提取关键的故障信息或模拟数据可能对极罕见的失败检测有很大帮助。该项目集中在开发一个主动学习框架上，该框架在复杂的相互作用设计参数，制造变化和操作条件的高维空间中智能采样，实现了对稀有电路故障的数据有效检测的目标。有针对性的主动学习框架将由机器学习模型基础的开发和可靠的学习方法来支持，这些方法可以扩展到高维参数空间。该项目的关键目的是通过从少量可用数据中提取最大可能的有用信息，对基本故障机制进行极度稀有的故障发现和识别。拟议的极其稀有的失败发现工作将广泛适用于对模拟，混合信号，射频和记忆循环的验证和失败分析，具有严格的失败规格以及许多其他类型的关键任务 - 任务 - 关键系统。这奖反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的crietia进行评估，可以通过评估来进行评估。