  Rapidly shrinking technology node and voltage scaling increase the
susceptibility of Soft Errors in digital circuits. Soft Errors are
radiation-induced effects while the radiation particles such as Alpha, Neutrons
or Heavy Ions, interact with sensitive regions of microelectronic
devices/circuits. The particle hit could be a glancing blow or a penetrating
strike. A well apprehended and characterized way of analyzing soft error
effects is the fault-injection campaign, but that typically acknowledged as
time and resource-consuming simulation strategy. As an alternative to
traditional fault injection-based methodologies and to explore the
applicability of modern graph based neural network algorithms in the field of
reliability modeling, this paper proposes a systematic framework that explores
gate-level abstractions to extract and exploit relevant feature representations
at low-dimensional vector space. The framework allows the extensive prediction
analysis of SEU type soft error effects in a given circuit. A scalable and
inductive type representation learning algorithm on graphs called GraphSAGE has
been utilized for efficiently extracting structural features of the gate-level
netlist, providing a valuable database to exercise a downstream machine
learning or deep learning algorithm aiming at predicting fault propagation
metrics. Functional Failure Rate (FFR): the predicted fault propagating metric
of SEU type fault within the gate-level circuit abstraction of the 10-Gigabit
Ethernet MAC (IEEE 802.3) standard circuit.
