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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

基本信息

批准号：
1956219
负责人：
Xiaoning Qian
金额：
$ 56.7万
依托单位：
Texas A&M University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956219&HistoricalAwards=false
关键词：
Collaborative Research SHF Medium Data

项目摘要

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.

尽管很长一段时间以来，电子产品的激增一直是由计算和消费应用驱动的，但集成电路(IC)目前正在加速集成到医疗保健、交通运输、机器人和自主系统中。除了提供规定的传感、计算和处理功能外，这些IC还必须满足严格的可靠性要求，以确保部署的整个关键任务系统的性能和安全。被设计为故障安全的电路故障发生率很低。然而，在典型的验证和测试程序下没有故障的迹象不能保证满足给定的几乎为零或极低的故障规范。另一方面，通过暴力故障检测可能永远无法实现穷举，这导致了不可接受的高昂的模拟和测试成本。该项目将开发高效的机器学习技术，用于极其罕见的电路故障检测，而不需要大量昂贵的模拟或测试数据。拟议的技术将使对可靠性关键IC和任务关键系统的总体验证和测试具有成本效益。这项研究还将使加州大学圣巴巴拉分校和达拉斯分校的两个团队能够教育和培训本科生和研究生，包括女性和代表性不足的群体，从而扩大美国技术劳动力的规模，并为其做出贡献。人们认为，在可用测量或模拟数据的实际范围内通过机器学习提取关键故障信息可以在很长一段时间内实现极其罕见的故障检测。该项目的核心是开发一种主动学习框架，该框架在复杂的交互设计参数、制造变量和操作条件的高维空间中智能采样，实现数据高效检测罕见电路故障的目标。有针对性的主动学习框架将得到机器学习模型基础和可扩展到高维参数空间的稳健学习方法的发展的支持。这个项目的主要目标是通过从少量可用数据中提取尽可能多的有用信息，使极其罕见的故障发现和潜在故障机制的识别实际上是可行的。拟议的极罕见故障发现工作将广泛适用于具有严格故障规范的模拟、混合信号、射频和存储器电路以及许多其他类型的关键任务系统的验证和故障分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。