The mimicking of classical conditioning, including acquisition, extinction, recovery, and generalization, can be efficiently achieved by using a single flexible memristor. In particular, the ...experiment of Pavlov's dog is successfully demonstrated. This demonstration paves the way for reproducing advanced neural processes and provides a frontier approach to the design of artificial‐intelligence systems with dramatically reduced complexity.
If a three-dimensional physical electronic system emulating synapse networks could be built, that would be a significant step toward neuromorphic computing. However, the fabrication complexity of ...complementary metal-oxide-semiconductor architectures impedes the achievement of three-dimensional interconnectivity, high-device density, or flexibility. Here we report flexible three-dimensional artificial chemical synapse networks, in which two-terminal memristive devices, namely, electronic synapses (e-synapses), are connected by vertically stacking crossbar electrodes. The e-synapses resemble the key features of biological synapses: unilateral connection, long-term potentiation/depression, a spike-timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow-power consumption. The three-dimensional artificial synapse networks enable a direct emulation of correlated learning and trainable memory capability with strong tolerances to input faults and variations, which shows the feasibility of using them in futuristic electronic devices and can provide a physical platform for the realization of smart memories and machine learning and for operation of the complex algorithms involving hierarchical neural networks.High-density information storage calls for the development of modern electronics with multiple stacking architectures that increase the complexity of three-dimensional interconnectivity. Here, Wu et al. build a stacked yet flexible artificial synapse network using layer-by-layer solution processing.
A reconfigurable neural network vision sensor is proposed by using gate-tunable photoresponse of van der Waals heterostructures.
Early processing of visual information takes place in the human ...retina. Mimicking neurobiological structures and functionalities of the retina provides a promising pathway to achieving vision sensor with highly efficient image processing. Here, we demonstrate a prototype vision sensor that operates via the gate-tunable positive and negative photoresponses of the van der Waals (vdW) vertical heterostructures. The sensor emulates not only the neurobiological functionalities of bipolar cells and photoreceptors but also the unique connectivity between bipolar cells and photoreceptors. By tuning gate voltage for each pixel, we achieve reconfigurable vision sensor for simultaneous image sensing and processing. Furthermore, our prototype vision sensor itself can be trained to classify the input images by updating the gate voltages applied individually to each pixel in the sensor. Our work indicates that vdW vertical heterostructures offer a promising platform for the development of neural network vision sensor.
In the nervous system, dendrites, branches of neurons that transmit signals between synapses and soma, play a critical role in processing functions, such as nonlinear integration of postsynaptic ...signals. The lack of these critical functions in artificial neural networks compromises their performance, for example in terms of flexibility, energy efficiency and the ability to handle complex tasks. Here, by developing artificial dendrites, we experimentally demonstrate a complete neural network fully integrated with synapses, dendrites and soma, implemented using scalable memristor devices. We perform a digit recognition task and simulate a multilayer network using experimentally derived device characteristics. The power consumption is more than three orders of magnitude lower than that of a central processing unit and 70 times lower than that of a typical application-specific integrated circuit chip. This network, equipped with functional dendrites, shows the potential of substantial overall performance improvement, for example by extracting critical information from a noisy background with significantly reduced power consumption and enhanced accuracy.
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.
Interest in resistance switching is currently growing apace. The promise of novel high‐density, low‐power, high‐speed nonvolatile memory devices is appealing enough, but beyond that there are ...exciting future possibilities for applications in hardware acceleration for machine learning and artificial intelligence, and for neuromorphic computing. A very wide range of material systems exhibit resistance switching, a number of which—primarily transition metal oxides—are currently being investigated as complementary metal–oxide–semiconductor (CMOS)‐compatible technologies. Here, the case is made for silicon oxide, perhaps the most CMOS‐compatible dielectric, yet one that has had comparatively little attention as a resistance‐switching material. Herein, a taxonomy of switching mechanisms in silicon oxide is presented, and the current state of the art in modeling, understanding fundamental switching mechanisms, and exciting device applications is summarized. In conclusion, silicon oxide is an excellent choice for resistance‐switching technologies, offering a number of compelling advantages over competing material systems.
Silicon oxide has long played a vital role in semiconductor microelectronics. Recent work is summarized showing how this most complementary metal–oxide–semiconductor (CMOS)‐compatible of oxides is a leading contender in the race to develop resistance‐switching technologies for nonvolatile memories, neuromorphic systems, and other exciting applications.
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.
As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for ...neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.
A comprehensive overview of biological and artificial neural networks is presented, including their key computing elements and related important functions, such as synapses, neurons, plasticity, learning, and memory, along with their electronic demonstrations using emerging devices. As a perspective, the connections and gaps between them and the challenges for building more bio‐plausible artificial neural networks are discussed.
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