Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Abstract

Traditional von Neumann computing architecture with separated computation and storage units has already impeded the data processing performance and energy efficiency, calling for emerging neuromorphic electronic and optical devices and systems which can mimic the human brain to shift this paradigm. Material-level innovation has become the key component to this revolution of information technology. Chalcogenide phase-change material (PCM) as a well-acknowledged data-storage medium is a promising candidate to tackle this challenge. In this review, the use of PCMs to implement artificial neurons and synapses from both the electronic and optical respects is discussed, and in particular, the structure-property physics and transition dynamics that enable such brain-inspired and in-memory computing applications are emphasized. Recent advances on the atomic-level amorphous and crystalline structures, transition mechanisms, materials optimization and design, neural and synaptic devices, brain-inspired chips, and computing systems, as well as the future opportunities of PCMs, are summarized and discussed.

High‐Throughput Screening for Phase‐Change Memory Materials

“…[4] With the efforts of Intel and Micron, Optane memories based on 3D Xpoint technology, a PCM technology, have entered the market. [5,6] Furthermore, PCM technology is showing a great potential in areas such as inmemory computing, [7] artificial neuromorphic systems, [8,9] and all-optical on-chip memory. [10] Since Ovshinsky originally proposed this technology in the 1960s, [11] there are some materials examined for phase-change recording applications, of which Ge-Sb-Te (GST) alloys perhaps are the best known.…”

A Brain-Inspired Homeostatic Neuron Based on Phase-Change Memories for Efficient Neuromorphic Computing

“…In this framework, several neuromorphic spiking neural networks (SNNs) based on CMOS technology have been proposed, demonstrating VLSI synaptic circuits with homeostatic neurons (Bartolozzi and Indiveri, 2006 ; Chicca et al, 2014 ; Qiao et al, 2017 ) and reward-based decision-making circuits (Wunderlich et al, 2019 ; Yan et al, 2019 ). At the same time, non-volatile memory devices, such as phase change memory (PCM), have raised considerable interest as promising synaptic connections for neuromorphic computation, thanks to the 3D stacking capability, the low-voltage operation and the ability to serve as embedded non-volatile memory in computing systems (Suri et al, 2012 ; Xu et al, 2020 ; Ren et al, 2021 ). In particular, PCMs have recently demonstrated outstanding multi-level capability (Kuzum et al, 2013 ; Ren et al, 2021 ), which enables continual learning in neural networks (Bianchi et al, 2019 ; Muñoz-Martín et al, 2019 ) and decision making in brain-inspired cognitive systems (Eryilmaz et al, 2014 ).…”

Numerical Study on Resistance Change Characteristics of Phase Change Materials

“…Even though PCMs are well studied, research on PCMs is still a hotspot, such as the study on their behavior under high pressure [5] and the study on the structural changes of GST upon rapid cooling by abinitio molecular dynamics simulations and atomistic cluster alignment analysis [6], and the research on their applications in other fields as well. For instance, Wainstein et al [7] investigated RF switches based on phase change memory (PCM) and Xu et al [8] discussed the use of PCMs to implement artificial neurons and synapses.…”