LSTM-based Analysis of Temporally- and Spatially-Correlated Signatures for Intermittent Fault Detection

Abstract

Intermittent faults are a critical reliability threat in deep submicron VLSI circuits. These faults occur non-deterministically due to unstable hardware and unpredictable operating conditions; they are activated/deactivated with changes in the runtime environment. Online fault prediction models are commonly used to predict soft errors and aging effects. A small set of flip-flops, whose states constitute the signature, conveys information about the fine-grained behavior of the circuit, and serves as the input to a machine-learning (ML) model. The nondeterministic failure mechanisms of intermittent faults, however, result in temporally- and spatially-correlated signatures (TSC-signatures). Moreover, the high-dimensional time-series features impede the use of traditional ML models for intermittent-fault detection. To cope with this challenge, we adapt the TSC-signatures to existing ML detection models. Moreover, we propose a novel detection model based on Recurrent Neural Network with Long Short-Term Memory (LSTM) that is inherently suitable for this problem. Simulation results for the ITC99 benchmark circuits highlight the effectiveness of the proposed model.