The accumulation and extrusion of Ca 2+ in the pre-and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca 2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy 2 minimization, closely resembling synaptic influx and extrusion of Ca 2+ , respectively.The diffusive memristor and its dynamics enable a direct emulation of both short-and long-term plasticity of biological synapses and represent a major advancement in hardware implementation of neuromorphic functionalities.CMOS circuits have been employed to mimic synaptic Ca 2+ dynamics, but three-terminal devices bear limited resemblance to bio-counterparts at the mechanism level and require significant numbers and complex circuits to simulate synaptic behavior [1][2][3] . A substantial reduction in footprint, complexity and energy consumption can be achieved by building a two-terminal circuit element, such as a memristor directly incorporating Ca 2+ -like dynamics.Various types of memristors based on ionic drift (drift-type memristor) 4-8 have recently been utilized for this purpose in neuromorphic architectures [9][10][11][12][13][14][15] . Although qualitative synaptic functionality has been demonstrated, the fast switching and non-volatility of drift memristors optimized for memory applications do not faithfully replicate the nature of plasticity. Similar issues also exist in MOS-based memristor emulators [16][17][18] , although they are capable of simulating a variety of synaptic functions including spike-timing-dependent plasticity (STDP). Recently, Lu's group adopted second-order drift memristors to approximate the Ca 2+ dynamics of chemical synapses by utilizing thermal dissipation 19 or mobility decay 20 , which successfully demonstrated STDP with non-overlapping spikes and other synaptic functions, representing a significant step towards bio-realistic synaptic devices. This approach features repeatability and simplicity, but the significant differences of the dynamical response from actual synapses limit the fidelity and variety of desired synaptic functions. A device with similar physical behavior as the biological Ca 2+ dynamics would enable improved emulation of synaptic function and broad applications to neuromorphic computing. Here we report such an emulator, which is a memristor based on metal atom 3 diffusion and spontaneous nanoparticle formation, as determined by in situ high-resolution transmission electron microscopy (HRTEM) and nanoparticle dynamics simulations. The dynamical properties of the diffusive memristors were confirmed to be functionally equivalent to Ca 2+ in bio-synapses, and their operating characteri...

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A novel Ag/oxide‐based threshold switching device with attractive features including ≈1010 nonlinearity is developed. High‐resolution transmission electron microscopic analysis of the nanoscale crosspoint device suggests that elongation of an Ag nanoparticle under voltage bias followed by spontaneous reformation of a more spherical shape after power off is responsible for the observed threshold switching.

The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation of universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity, and power consumption. The random bits generated by the diffusive memristor true random number generator pass all 15 NIST randomness tests without any post-processing, a first for memristive-switching true random number generators. Based on nanoparticle dynamic simulation and analytical estimates, we attribute the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of the Internet of Things.

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...

Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with “leaky integrate-and-fire” dynamics augmented by a signal gain on the output. Paired with non-volatile pseudo-memcapacitive synapses, a Hebbian-like learning mechanism is implemented in a capacitive switching network, leading to the observed associative learning. A prototypical fully integrated capacitive neural network is built and used to classify inputs of signals.

We demonstrate a new thin-film graphene diode called a geometric diode that relies on geometric asymmetry to provide rectification at 28 THz. The geometric diode is coupled to an optical antenna to form a rectenna that rectifies incoming radiation. This is the first reported graphene-based antenna-coupled diode working at 28 THz, and potentially at optical frequencies. The planar structure of the geometric diode provides a low RC time constant, on the order of 10 −15 s, required for operation at optical frequencies, and a low impedance for efficient power transfer from the antenna. Fabricated geometric diodes show asymmetric current-voltage characteristics consistent with Monte Carlo simulations for the devices. Rectennas employing the geometric diode coupled to metal and graphene antennas rectify 10.6 µm radiation, corresponding to an operating frequency of 28 THz. The graphene bowtie antenna is the first demonstrated functional antenna made using graphene. Its response indicates that graphene is a suitable terahertz resonator material. Applications for this terahertz diode include terahertz-wave and optical detection, ultra-high-speed electronics and optical power conversion.

also been shown to be accurate, fast, and efficient when implementing object recognition or detection on neuromorphic hardware platforms. [8] It is then natural to look for a middle path by consolidating the advantages of these two types of networks in a single computing system. The key to bridging the gap between continuous valued ANNs and neuromorphic spiking networks is the necessity to develop SNNs that can match the error rates of their continuous valued ANNs. There have been a few efforts toward this direction such as training SNNs using backpropagation, [9] implementing SNN classification layers using stochastic gradient descent [10] or modifying the transfer function of ANN during training so that the network parameters can be mapped to the SNN. [4,11] Although these results are promising, these methods are not sufficiently efficient to train a spiking architecture of the size of VGG-16 yet. A seemingly easier approach would be to take the outputs of a pretrained ANN and then map them to an equivalently accurate SNN. There have been a few efforts in the field of ANN-SNN conversion. In one case, the convolutional neural network (CNN) units were translated into biologically inspired spiking units with leaks and refractory periods. [12] In another report, nearly lossless conversion of ANNs for the Modified National Institute of Standards and Technology (MNIST) [13] classification task was achieved by using a weight normalization scheme. [6] This scheme is based on the principle of rescaling the weights to avoid approximation errors in SNNs due to either excessive or too little firing of the neurons. Researchers in IBM demonstrated an approach that optimized CNNs for the TrueNorth platform, which has binary weights and restricted connectivity. [14] In another study along similar lines, a conversion method was developed which involved spiking neurons that adapted their firing threshold to reduce the number of spikes needed to encode information. [15] However, such studies were all limited to conventional complimentary metal oxide semiconductor (CMOS)-based hardware and not much effort has been invested in taking advantage of this relatively important new paradigm (ANN-SNN conversion) and the inherent advantages possibly offered by emerging nanoscale devices, such as memristors.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aelm.201900060. Artificial Neural NetworksThe bar for state of the art classification error rates has been pushed to new levels by GoogLeNet [1] and VGG-16 [2] for computer vision benchmarks such as ImageNet. [3] Artificial neural networks (ANNs) have shown remarkable performance in performing tasks of practical importance such as image recognition, edge detection, decision making, sequence recognition, and playing Go games, just to name a few. On the other hand, spiking neural networks (SNNs) are very effective at reducing the latency and computational load of deep neural networks. [4,5] SNNs can output results even after the ...