Machine Learning to Tackle the Challenges of Transient and Soft Errors in Complex Circuits
This work addresses the problem of high computational costs in circuit reliability analysis for engineers, though it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackles the challenge of efficiently analyzing functional failure rates in complex circuits by using machine learning to predict per-instance Functional De-Rating data, achieving accurate predictions that reduce reliance on computationally intensive fault-injection simulations.
The Functional Failure Rate analysis of today's complex circuits is a difficult task and requires a significant investment in terms of human efforts, processing resources and tool licenses. Thereby, de-rating or vulnerability factors are a major instrument of failure analysis efforts. Usually computationally intensive fault-injection simulation campaigns are required to obtain a fine-grained reliability metrics for the functional level. Therefore, the use of machine learning algorithms to assist this procedure and thus, optimising and enhancing fault injection efforts, is investigated in this paper. Specifically, machine learning models are used to predict accurate per-instance Functional De-Rating data for the full list of circuit instances, an objective that is difficult to reach using classical methods. The described methodology uses a set of per-instance features, extracted through an analysis approach, combining static elements (cell properties, circuit structure, synthesis attributes) and dynamic elements (signal activity). Reference data is obtained through first-principles fault simulation approaches. One part of this reference dataset is used to train the machine learning model and the remaining is used to validate and benchmark the accuracy of the trained tool. The presented methodology is applied on a practical example and various machine learning models are evaluated and compared.