The rapid increase in information in the big-data era calls for changes to information-processing paradigms, which, in turn, demand new circuit-building blocks to overcome the decreasing ...cost-effectiveness of transistor scaling and the intrinsic inefficiency of using transistors in non-von Neumann computing architectures. Accordingly, resistive switching materials (RSMs) based on different physical principles have emerged for memories that could enable energy-efficient and area-efficient in-memory computing. In this Review, we survey the four physical mechanisms that lead to such resistive switching: redox reactions, phase transitions, spin-polarized tunnelling and ferroelectric polarization. We discuss how these mechanisms equip RSMs with desirable properties for representation capability, switching speed and energy, reliability and device density. These properties are the key enablers of processing-in-memory platforms, with applications ranging from neuromorphic computing and general-purpose memcomputing to cybersecurity. Finally, we examine the device requirements for such systems based on RSMs and provide suggestions to address challenges in materials engineering, device optimization, system integration and algorithm design.Resistive switching materials enable novel, in-memory information processing, which may resolve the von Neumann bottleneck. This Review focuses on how the switching mechanisms and the resultant electrical properties lead to various computing applications.
Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory ...array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.
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.
In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large ...time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.
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 NbO
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.
A nociceptor is a critical and special receptor of a sensory neuron that is able to detect noxious stimulus and provide a rapid warning to the central nervous system to start the motor response in ...the human body and humanoid robotics. It differs from other common sensory receptors with its key features and functions, including the "no adaptation" and "sensitization" phenomena. In this study, we propose and experimentally demonstrate an artificial nociceptor based on a diffusive memristor with critical dynamics for the first time. Using this artificial nociceptor, we further built an artificial sensory alarm system to experimentally demonstrate the feasibility and simplicity of integrating such novel artificial nociceptor devices in artificial intelligence systems, such as humanoid robots.
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.
Volatile threshold switches with Ag or Cu active metals in solid electrolytes feature electrical‐bias‐induced conduction channel formation and spontaneous rupture of the conduction channel upon cessation of the external bias, which shows unique delay and relaxation dynamics in conductance evolution and can be tuned for a variety of novel electronic applications, including selectors, synapses, neurons, true random number generators, etc.
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.
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units have led to major advances in artificial intelligence. However, state-of-the-art LSTM models with ...significantly increased complexity and a large number of parameters have a bottleneck in computing power resulting from both limited memory capacity and limited data communication bandwidth. Here we demonstrate experimentally that the synaptic weights shared in different time steps in an LSTM can be implemented with a memristor crossbar array, which has a small circuit footprint, can store a large number of parameters and offers in-memory computing capability that contributes to circumventing the ‘von Neumann bottleneck’. We illustrate the capability of our crossbar system as a core component in solving real-world problems in regression and classification, which shows that memristor LSTM is a promising low-power and low-latency hardware platform for edge inference.Deep neural networks are increasingly popular in data-intensive applications, but are power-hungry. New types of computer chips that are suited to the task of deep learning, such as memristor arrays where data handling and computing take place within the same unit, are required. A well-used deep learning model called long short-term memory, which can handle temporal sequential data analysis, is now implemented in a memristor crossbar array, promising an energy-efficient and low-footprint deep learning platform.
Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time‐ and energy‐efficient ...computational paradigms for the Internet‐of‐Things and edge computing. Nonvolatile electrolyte‐gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large‐scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide‐based EGT employing amorphous Nb2O5 and LixSiO2 is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi‐linear update, good endurance (106) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm−2). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT‐based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide‐based EGT devices for energy‐efficient neuromorphic computing to support edge application.
An oxide‐based electrolyte‐gated transistor (EGT) with prominent analog switching performance is introduced and integrated into a 32 × 32 array. The EGT array is leveraged for hardware implementation of spiking neural networks to process spatiotemporal information and is further deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide‐based EGT devices for energy‐efficient neuromorphic computing to support edge application.
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