Nanoscale inorganic electronic synapses or synaptic devices, which are capable of emulating the functions of biological synapses of brain neuronal systems, are regarded as the basic building blocks for beyond-Von Neumann computing architecture, combining information storage and processing. Here, we demonstrate a Ag/AgInSbTe/Ag structure for chalcogenide memristor-based electronic synapses. The memristive characteristics with reproducible gradual resistance tuning are utilised to mimic the activity-dependent synaptic plasticity that serves as the basis of memory and learning. Bidirectional long-term Hebbian plasticity modulation is implemented by the coactivity of pre- and postsynaptic spikes, and the sign and degree are affected by assorted factors including the temporal difference, spike rate and voltage. Moreover, synaptic saturation is observed to be an adjustment of Hebbian rules to stabilise the growth of synaptic weights. Our results may contribute to the development of highly functional plastic electronic synapses and the further construction of next-generation parallel neuromorphic computing architecture.

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.

Stochastic neurons based on CuS/GeSe conductive bridge threshold switching memristors are designed to mimic the probabilistic computing ability of the brain at a hardware level.

We analyzed the tight Cr clusters in amorphous CrGeTe3 to understand the mechanism of this abnormal phase change material.

Phase‐change material (PCM) devices are one of the most mature nonvolatile memories. However, their high power consumption remains a bottleneck problem limiting the data storage density. One may drastically reduce the programming power by patterning the PCM volume down to nanometer scale, but that route incurs a stiff penalty from the tremendous cost associated with the complex nanofabrication protocols required. Instead, here a materials solution to resolve this dilemma is offered. The authors work with memory cells of conventional dimensions, but design/exploit a PCM alloy that decomposes into a heterogeneous network of nanoscale crystalline domains intermixed with amorphous ones. The idea is to confine the subsequent phase‐change switching in the interface region of the crystalline nanodomain with its amorphous surrounding, forming/breaking “nano‐bridges” that link up the crystalline domains into a conductive path. This conductive‐bridge switching mechanism thus only involves nanometer‐scale volume in programming, despite of the large areas in contact with the electrodes. The pore‐like devices based on spontaneously phase‐separated Ge13Sb71O16 alloy enable a record‐low programming energy, down to a few tens of femtojoule. The new PCM/fabrication is fully compatible with the current 3D integration technology, adding no expenses or difficulty in processing.

Thermal conductivity of chalcogenide material with superlatticelike (SLL) structure is investigated using the 3ω method and the molecular dynamics method. Both the measured and calculated results show that the thermal conductivity of SLL is lower than those of conventional chalcogenide materials and will decrease to a minimum as the number of interfaces increases. The Raman spectrum is introduced to study the phonon behavior of SLL and the “phonon mode vanishing” is proposed to explain its lower thermal conductivity. Finite-element analysis and phase change memory cell testing confirm the enhancement of cell performance for SLL with minimum thermal conductivity.

Ge-Sb-Te (GST)-based phase-change memory (PCM) excels in the switching performance but remains insufficient of the operating speed to replace cache memory (the fastest memory in a computer). In this work, a novel approach using Sb2Te3 templates is proposed to boost the crystallization speed of GST by five times faster. This is because such a GST/Sb2Te3 heterostructure changes the crystallizing mode of GST from the nucleation-dominated to the faster growth-dominated one, as confirmed by high-resolution transmission electron microscopy, which captures the interface-induced epitaxial growth of GST on Sb2Te3 templates in devices. Ab initio molecular dynamic simulations reveal that Sb2Te3 templates can render GST sublayers faster crystallization speed because Sb2Te3’s “sticky” surface contains lots of unpaired electrons that may attract Ge atoms with less antibonding interactions. Our work not only proposes a template-assisted PCM with fast speed but also uncovers the hidden mechanism of Sb2Te3’s sticky surface, which can be used for future material selection.

The fully memristive neural network is emerging as a game‐changer in the artificial intelligence competition. Artificial synapses and neurons, as two fundamental elements for hardware neural networks, have been substantially implemented by different devices with memory and threshold switching (TS) behaviors, respectively. However, obtaining controllable memory and TS behaviors in the same memristive material system is still a considerable challenge that holds great potential for realizing compatible artificial neurons and synapses. Here, a heterogeneous bilayer conductive filamentary memristor comprising two different electrolytes with distinct copper ion mobility is reported: Cu/GeTe/Al2O3/Pt, which can demonstrate the governance of switching types. Experimentally, when the thickness of the Al2O3 layer is 3 nm, stable nonvolatile multilevel memory switching (MS) is observed and employed to mimic the synaptic plasticity. With increasing oxide thickness, the switching behavior under the same compliance current alters from MS to volatile TS and is used to emulate the integrate‐and‐fire neuron function. The controllable switching stems from the change in the metal filament morphology within the Al2O3 layer, which is supported by ab initio calculation results. This method enables a new pathway for constructing functionally reconfigurable neuromorphic devices for intelligence neuromorphic systems.