  De-Rating or Vulnerability Factors are a major feature of failure analysis
efforts mandated by today's Functional Safety requirements. Determining the
Functional De-Rating of sequential logic cells typically requires
computationally intensive fault-injection simulation campaigns. In this paper a
new approach is proposed which uses Machine Learning to estimate the Functional
De-Rating of individual flip-flops and thus, optimising and enhancing fault
injection efforts. Therefore, first, a set of per-instance features is
described and extracted through an analysis approach combining static elements
(cell properties, circuit structure, synthesis attributes) and dynamic elements
(signal activity). Second, reference data is obtained through first-principles
fault simulation approaches. Finally, one part of the reference dataset is used
to train the Machine Learning algorithm and the remaining is used to validate
and benchmark the accuracy of the trained tool. The intended goal is to obtain
a trained model able to provide 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 presented methodology is accompanied by a
practical example to determine the performance of various Machine Learning
models for different training sizes.
