Reliability Analysis of Phase Change Memory-Based Neuromorphic Circuits

This paper presents an in-depth reliability analysis of neuromorphic circuits utilizing phase change memory (PCM). In-memory computing with PCM cells to store synaptic weights is exploited to address the performance gap between processing needs and memory constraints in machine learning computation. The integration of PCM for hardware realization of spiking neural networks (SNNs) enhances data processing and transmission capabilities. However, challenges remain in memristor-based crossbar architectures, including the effects of voltage drops and current fluctuations on the reliability of PCM. Hence, a reliability model accounting for process variations and temperature influences is proposed. The model effectively predicts the reliability of PCM in neuromorphic circuits, paving the way for more robust cognitive computing applications.