Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfactory solutions for neural networks. Recently, several types of memristor devices have become popular alternatives because of their outstanding characteristics such as scalability, high performance, and non-volatility. To understand the characteristics of memristors, a comparison among memristors has been made, considering both maturity and performance. Magneto-resistance random access memory, phase-change random access memory, and resistive random access memory among the proposed memristors are good candidates as synaptic devices for weight storage and matrixvector multiplication required in artificial neural networks (ANNs). Moreover, these devices play key roles as synaptic devices in research for bio-plausible spiking neural networks (SNNs) because their distinctive switching properties are well matched for emulating synaptic and neuron functions of biological neural networks. In this paper we review motivation, advantage, technology, and applications of memristor devices for neural networks from practical approaches of ANNs to futuristic research of SNNs, considering the current status of memristor technology.

Phase-change memory (PCM), a non-volatile memory technology, is considered the most promising candidate for storage class memory and neuro-inspired devices. It is generally fabricated based on GeTe–Sb2Te3 pseudo-binary alloys. However, natively, it has technical limitations, such as noise and drift in electrical resistance and high current in operation for real-world device applications. Recently, heterogeneously structured PCMs (HET-PCMs), where phase-change materials are hetero-assembled with functional (barrier) materials in a memory cell, have shown a dramatic enhancement in device performance by reducing such inherent limitations. In this Perspective, we introduce recent developments in HET-PCMs and relevant mechanisms of operation in comparison with those of conventional alloy-type PCMs. We also highlight corresponding device enhancements, particularly their thermal stability, endurance, RESET current density, SET speed, and resistance drift. Last, we provide an outlook on promising research directions for HET-PCMs including PCM-based neuromorphic computing.

No abstract

No abstract

No abstract