GOALI: A Machine-Learning Approach to Built-In Self-Test of Mixed-Signal/RF Circuits

ECS-0622081Y。我们将为混合信号/射频电路开发内建自测试(BIST)解决方案。BIST是电子线路的一项非常重要的能力，它允许他们在运行领域检查自己的运行状况并报告潜在的故障。目前最先进的BIST缺乏针对混合信号/射频电路的解决方案，主要是因为这些电路的大多数已知测试方法依赖于功能测试方法，而这种方法不可能在芯片上实现。为了缓解这个问题，在这个项目中，我们将采用基于机器学习的替代测试方法，其中通过具有代表性的芯片群体训练的神经分类器将检查一组简单的测量结果，并将决定芯片是否健康。将致力于两个主要领域，即用于产生测试刺激和获取判别性测量的片上电路的设计，以及用于片上机器学习的神经分类器的设计。为了验证基于机器学习的内建自测试的可行性和有效性，将通过该项目的工业合作伙伴国家半导体公司设计和制造两个混合信号/射频集成电路，即开关电容滤波器和低噪声放大器。拟议的研究将与各种教育和外展活动相辅相成，包括开发一个新的研究生级别的研讨会，讨论机器学习在计算机辅助设计和测试中的应用，本科生参与研究，以及在可测试和可靠的电子产品设计中促进主动学习。智力优势：该项目旨在为混合信号/射频电路开发内置自测试(BIST)解决方案。BIST是电子线路的一项非常重要的能力，它允许他们在运行领域检查自己的运行状况并报告潜在的故障。目前最先进的BIST缺乏针对混合信号/射频电路的解决方案，主要是因为这些电路的大多数已知测试方法依赖于功能测试方法，而这种方法不可能在芯片上实现。为了缓解这个问题，这个项目遵循了一种基于机器学习的替代测试方法，在这种方法中，神经分类器通过具有代表性的芯片群体进行训练，检查一组简单的测量结果，并决定芯片是否健康。将致力于两个主要领域，即用于产生测试刺激和获取判别性测量的片上电路的设计，以及用于片上机器学习的神经分类器的设计。将设计和制造两个混合信号/射频集成电路，以展示基于机器学习的BIST的可行性和有效性。广泛影响：该项目将促进实现可测试和可靠的电子电路和系统，从而扩大其在广泛应用中的部署，实现可靠的计算，并培养技术可信性。与拟议的研究相辅相成的是各种教育和外联活动，包括开发一个新的研究生级别的研讨会，讨论机器学习在计算机辅助设计和测试中的应用，让本科生参与研究，并通过参与耶鲁大学太阳能赛车队Lux来促进在可靠电子产品设计方面的积极学习。

ECS-0622081Y. Makris, Yale UniversityWe will develop Built-In Self-Test (BIST) solutions for mixed-signal/RF circuits. BIST is a very important capability of electronic circuits, which allows them to examine their operational health in the field of operation and report potential malfunctions. The current state-of-the-art lacks BIST solutions for mixed-signal/RF circuits, mainly because most known test methods for these circuits rely on a functional test approach, which is impossible to implement on-chip. To mitigate this problem, in this project we will follow an alternative test approach based on machine-learning, wherein a neural classifier, trained through a representative chip population, will examine a set of simple measurements and will decide whether the chip is healthy or not. Efforts will be directed to two main areas, namely the design of on-chip circuitry for generation of test stimuli and acquisition of discriminative measurements and the design of neural classifiers for on-chip machine learning. Two mixed-signal/RF integrated circuits, namely a switched-capacitor filter and a low-noise amplifier will be designed and fabricated through National Semiconductor, the industrial collaborator of this project, in order to demonstrate the feasibility and effectiveness of machine learning-based BIST. The proposed research will be complemented by various educational and outreach activities, including the development of a new graduate-level seminar on Applications of Machine-Learning in Computer Aided Design and Test, participation of undergraduates in research, and promotion of active learning in the design of testable and reliable electronics.Intellectual Merit: This project aims to develop Built-In Self-Test (BIST) solutions for mixed-signal/RF circuits. BIST is a very important capability of electronic circuits, which allows them to examine their operational health in the field of operation and report potential malfunctions. The current state-of-the-art lacks BIST solutions for mixed-signal/RF circuits, mainly because most known test methods for these circuits rely on a functional test approach, which is impossible to implement on-chip. To mitigate this problem, this project follows an alternative test approach based on machine-learning, wherein a neural classifier, trained through a representative chip population, examines a set of simple measurements and decides whether the chip is healthy or not. Efforts will be directed to two main areas, namely the design of on-chip circuitry for generation of test stimuli and acquisition of discriminative measurements and the design of neural classifiers for on-chip machine learning. Two mixed-signal/RF integrated circuits will be designed and fabricated to demonstrate the feasibility and effectiveness of machine-learning-based BIST.Broader Impact: This project will facilitate the realization of testable and reliable electronic circuits and systems, thus extending their deployment in a broad range of applications, enabling reliable computing, and fostering technology trustworthiness. The proposed research is complemented by various educational and outreach activities, including the development of a new graduate-level seminar on Applications of Machine-Learning in Computer Aided Design and Test, participation of undergraduates in research, and promotion of active learning in the design of reliable electronics through involvement with the Yale University solar car racing Team Lux.