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Memristors are a leading candidate for future storage and neuromorphic computing technologies 1-10 due to characteristics such as device scalability, multi-state switching, fast switching speed, high switching endurance and CMOS compatibility 6,[11][12][13][14][15][16] . Most research and development efforts have been focused on improving device switching performance in optimal conditions, and the reliability of memristors in harsh environments such as at high temperature and on bending substrates has so far received much less attention. Since the programming processes in memristors based on traditional oxide materials mostly rely on ion moving and ionic valence changing 16,17 , the thermal instability at elevated temperatures could result in device failure 18 . Thus, to the best of our knowledge, there has been no reliable switching behaviours observed in memristors at temperatures above 200 °C 18,19 , which limits their potential application in harsh electronics such as those demanded in aerospace, military, automobile, geothermal, oil and gas industries. Common high temperature electronic materials, such as SiC and III-nitride 20,21 , are not adoptable in fabricating memristors, and therefore searching for new materials and structures for robust memristors with good performance is desirable.By stacking two-dimensional (2D) layered materials together 22-30 , van der Waals (vdW) heterostructures can combine the superior properties of each 2D component. 2D materials have shown excellent structural stability 31,32 and electrical properties, which could provide significant improvements in the robustness of electronic devices. For example, graphene possesses unparalleled breaking strength, and ultra-high thermal and chemical stabilities 33 ; molybdenum disulfide (MoS 2 ) has shown good flexibility, large Young's modulus (comparable to stainless steel), 34 and excellent thermal stability up to 1,100 °C 32 ; and various functionalized 2D material layers, or certain grain boundaries within 2D materials, have shown switching behaviours [35][36][37][38][39][40][41][42][43][44] . Since both the thickness and roughness of 2D layered materials can be controlled accurately at the atomic scale, the reliability and uniformity of the electronic devices based on such materials and their vdW heterostructures could also be optimized.In this Article, we report robust memristors based on a vdW heterostructure made of fully layered 2D materials (graphene/MoS 2−x O x / graphene), which exhibit repeatable bipolar resistive switching with endurance up to 10 7 and high thermal stability with an operating temperature of up to 340 °C. The MoS 2−x O x layer was found to be responsible for the high thermal stability of the devices by performing high temperature in situ high-resolution transmission electron microscopy (HRTEM) studies. Further in situ scanning transmission electron microscopy (STEM) investigations on the cross section of a functional device revealed a well-defined conduction channel and a switching mechanism based on the migr...

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Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.is applied due to the formation of a conduction channel(s) with Ag or Cu atoms. Unlike the ECM cells, the resistance recovers back spontaneously upon cessation of the external bias, yielding a superior I-V nonlinearity [4][5][6][7][8][9][10][11] and unique temporal conductance evolution dynamics. [7,12,13] Such a relaxation process is due to the physical dissolution of the metallic conduction channel under driving forces such as minimization of interfacial energy. In case active metals are used as electrodes, these metals may be doped into the dielectrics eventually under the combined effect of electric fields, thermal diffusion, which may lead to a reduced threshold voltage for the subsequent switching, similar to the process called "electroforming." The unique delay and relaxation dynamics of Ag and Cu-based threshold switches make them suitable for innovative applications in circuits and systems.Threshold switches with Ag or Cu active metals are also termed as "diffusive memristors" [7] to emphasize the underlying nature of the diffusive dynamics of the metal species. Factors including bias amplitude, biasing duration, as well as ambient temperature have been observed to have an impact on such a process, showing a wide range of dynamical properties, which could be exploited as access devices for memories with fast transition (e.g., <100 ns) or synaptic emulators with a relatively slower evolution (e.g., >1 µs). We survey the recently developed material systems which have exhibited this kind of threshold switching. New evidences by electron microscopy and quantitatively thermodynamic modeling are examined to give insights into the underlying physics of the mechanisms. We also discuss applications enabled by the advent of such threshold switches. Temporal Response of the SwitchingThe dynamical response of threshold switching is a critical property for many applications but has been characterized to a lesser extent. The temporal responses could be probed by applying voltage pulses and measuring the resulting currents in the time domain. It is a general observation in both ECM and threshold switches that the conductance experienced a transition from insulating state to conducting state after a finite time duration (delay time) under the external bias, as ill...

Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated.

Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbOx Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.

Reservoir computing (RC) is a framework that can extract features from a temporal input into a higher‐dimension feature space. The reservoir is followed by a readout layer that can analyze the extracted features to accomplish tasks such as inference and classification. RC systems inherently exhibit an advantage, since the training is only performed at the readout layer, and therefore they are able to compute complicated temporal data with a low training cost. Herein, a physical reservoir computing system using diffusive memristor‐based reservoir and drift memristor‐based readout layer is experimentally implemented. The rich nonlinear dynamic behavior exhibited by a diffusive memristor due to Ag migration and the robust in situ training of drift memristor arrays makes the combined system ideal for temporal pattern classification. It is then demonstrated experimentally that the RC system can successfully identify handwritten digits from the Modified National Institute of Standards and Technology (MNIST) dataset, achieving an accuracy of 83%.

This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including spiking neural networks, fully connected artificial neural networks, convolutional neural networks, and Hopfield recurrent neural networks. Challenges and strategies for nanoelectronic brain-inspired computing systems, including device variations, training, and testing algorithms, are also discussed.