Towards Evaluating SEU Type Soft Error Effects with Graph Attention Network

With the rapid development of integrated circuit manufacturing processes, soft errors have emerged as a pivotal factor that influences circuit reliability. This paper endeavors to investigate the rapid estimation of the impact of the single event upset (SEU) on the logic behaviors of flip-flops in a circuit using machine learning methods. A major challenge currently faced when applying machine learning methods for SEU evaluation is the absence of publicly available circuit datasets. Therefore, this paper employs the fault injection method to acquire circuit data such as soft error sensitivity. Subsequently, it models the gate-level netlist and integrates the netlist models with the acquired data to construct a dataset. Finally, a model based on graph at-tention network (GAT) is developed and we use the leave-one-out cross validation method to evaluate the performance. Compared to neural network methods skilled at handling structured data, the experimental results indicate that the method proposed in this paper has better predictive performance. It achieves an average absolute error of 0.064, representing a 43.46 % improvement over the baseline.