Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing.

The accumulation and extrusion of Ca2+ 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 Ca2+ 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 minimization, closely resembling synaptic influx and extrusion of Ca2+, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.

Programming memristor arrays with arbitrarily high precision for analog computing.

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.

Memristive Field-Programmable Analog Arrays for Analog Computing.

The increasing interests in analog computing nowadays call for multipurpose analog computing platforms with reconfigurability. The advancement of analog computing, enabled by novel electronic elements like memristors, has shown its potential to sustain the exponential growth of computing demand in the new era of analog data deluge. Here, a platform of a memristive field-programmable analog array (memFPAA) is experimentally demonstrated with memristive devices serving as a variety of core analog elements and CMOS components as peripheral circuits. The memFPAA is reconfigured to implement a first-order band pass filter, an audio equalizer, and an acoustic mixed frequency classifier, as application examples. The memFPAA, featured with programmable analog memristors, memristive routing networks, and memristive vector-matrix multipliers, opens opportunities for fast prototyping analog designs as well as efficient analog applications in signal processing and neuromorphic computing.

A memristor-based hybrid analog-digital computing platform for mobile robotics.

Algorithms for mobile robotic systems are generally implemented on purely digital computing platforms. Developing alternative computational platforms may lead to more energy-efficient and responsive mobile robotics. Here, we report a hybrid analog-digital computing platform enabled by memristors on a mobile inverted pendulum robot. Our mobile robotic system can tune the conductance states of memristors adaptively using a model-free optimization method to achieve optimal control performance. We implement sensor fusion and the motion control algorithms on our hybrid analog-digital computing platform and demonstrate more than one order of magnitude enhancement of speed and energy efficiency over traditional digital platforms.

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Capacitive neural network with neuro-transistors.

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.

Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors.

Memristors are promising building blocks for the next-generation memory and neuromorphic computing systems. Most memristors use materials that are incompatible with the silicon dominant complementary metal-oxide-semiconductor technology, and require external selectors in order for large memristor arrays to function properly. Here we demonstrate a fully foundry-compatible, all-silicon-based and self-rectifying memristor that negates the need for external selectors in large arrays. With a p-Si/SiO2/n-Si structure, our memristor exhibits repeatable unipolar resistance switching behaviour (105 rectifying ratio, 104 ON/OFF) and excellent retention at 300 °C. We further build three-dimensinal crossbar arrays (up to five layers of 100 nm memristors) using fluid-supported silicon membranes, and experimentally confirm the successful suppression of both intra- and inter-layer sneak path currents through the built-in diodes. The current work opens up opportunities for low-cost mass production of three-dimensional memristor arrays on large silicon and flexible substrates without increasing circuit complexity.

A novel true random number generator based on a stochastic diffusive memristor.

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.Memristors can switch between high and low electrical-resistance states, but the switching behaviour can be unpredictable. Here, the authors harness this unpredictability to develop a memristor-based true random number generator that uses the stochastic delay time of threshold switching.

Anatomy of Ag/Hafnia-Based Selectors with 1010 Nonlinearity.

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.

Correction: Electrochemical metallization switching with a platinum group metal in different oxides.

Correction for 'Electrochemical metallization switching with a platinum group metal in different oxides' by Zhongrui Wang, et al., Nanoscale, 2016, DOI: 10.1039/c6nr01085g.

Sub-10 nm Ta Channel Responsible for Superior Performance of a HfO2 Memristor.

Memristive devices are promising candidates for the next generation non-volatile memory and neuromorphic computing. It has been widely accepted that the motion of oxygen anions leads to the resistance changes for valence-change-memory (VCM) type of materials. Only very recently it was speculated that metal cations could also play an important role, but no direct physical characterizations have been reported yet. Here we report a Ta/HfO2/Pt memristor with fast switching speed, record high endurance (120 billion cycles) and reliable retention. We programmed the device to 24 discrete resistance levels, and also demonstrated over a million (2(20)) epochs of potentiation and depression, suggesting that our devices can be used for both multi-level non-volatile memory and neuromorphic computing applications. More importantly, we directly observed a sub-10 nm Ta-rich and O-deficient conduction channel within the HfO2 layer that is responsible for the switching. This work deepens our understanding of the resistance switching mechanism behind oxide-based memristive devices and paves the way for further device performance optimization for a broad spectrum of applications.