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

虽然电子产品的激增长期以来一直受到计算和消费者应用的推动，但集成电路（IC）目前正在加速集成到医疗保健、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规范，以保障部署的整个关键任务系统的性能和安全。设计为故障安全的电路表现出低故障发生率。然而，在典型的验证和测试程序下没有失败的迹象并不能保证满足给定的接近零或极低的失败规范。另一方面，穷举可能永远无法实现的蛮力故障检测，这将导致在一个不可接受的高成本的仿真和测试。该项目将开发高效的机器学习技术，用于极罕见的电路故障检测，而无需大量昂贵的仿真或测试数据。所提出的技术将使成本效益的验证和测试的可靠性关键集成电路和关键任务系统一般。 进行的研究还将使加州大学圣巴巴拉和德州大学达拉斯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，从而扩大和促进急需的美国技术劳动力。人们认为，在可用测量或模拟数据的实际限制内，通过机器学习提取关键故障信息可以大大有助于实现极其罕见的故障检测。该项目的核心是开发一个主动学习框架，该框架可以在复杂的交互设计参数、制造变化和操作条件的高维空间中智能采样，从而实现对罕见电路故障进行数据高效检测的目标。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强大学习方法的支持。该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，使极罕见的故障发现和潜在故障机制的识别切实可行。建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，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.