Fault Analysis for a MTJ-based Spiking Neural Network

Interest in Spiking Neural Networks (SNN), which mirror brain functionality within the Artificial Intelligence (AI) domain, stems from their potential for energy efficiency. Recent advancements have introduced novel synapse and neuron-implementation devices, promising further energy efficiency enhancements. However, the susceptibility of hardware-based SNNs to manufacturing defects raises concerns. This study investigates the impact of manufacturing defects on SNN performance, leveraging Magnetic Tunnel Junction (MTJ) devices for synapse and neuron implementation. Circuit-level simulations are conducted by building a basic SNN and intentionally introducing defects. Input spikes are applied to the SNN to evaluate its performance in the presence of defects. Furthermore, our investigation delves into the effects of timing alterations during the neuron's integration and leakage windows operations on defective SNNs. The results from our analysis offer valuable guidance for formulating fault models that generate high-quality test vectors.