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.
Owing to their attractive application potentials in both non-volatile memory and unconventional computing, memristive devices have drawn substantial research attention in the last decade. However, ...major roadblocks still remain in device performance, especially concerning relatively large parameter variability and limited cycling endurance. The response of the active region in the device within and between switching cycles plays the dominating role, yet the microscopic details remain elusive. This Review summarizes recent progress in scientific understanding of the physical origins of the non-idealities and propose a synergistic approach based on in situ characterization and device modeling to investigate switching mechanism. At last, the Review offers an outlook for commercialization viability of memristive technology.
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.
As semiconductor technology enters the more than Moore era, there exists an apparent contradiction between the rapidly growing demands for data processing and the visible inefficiency rooted in ...traditional computing architecture. Neuromorphic systems hold great prospects in enabling a new generation of computing paradigm that can address this issue, which demands device components with rich dynamics and nonlinearity. Herein, the nonlinearity in memristive devices and their application in building neuromorphic dynamic systems are reviewed. The internal mechanisms that endow memristive devices with nonlinearity and rich dynamics are reviewed and subsequently the nonlinear spiking neurons that are implemented utilizing the physical processes in memristors are shown. Typical examples on neuromorphic dynamic systems based on nonlinear memristors are summarized, including memristive reservoir, memristive oscillatory neural network, and memristive chaotic computing. Finally, an outlook in the development of neuromorphic dynamic systems is given.
Herein, the internal nonlinearity and rich dynamics in memristive devices, distinguishing them from static linear circuit elements, which provide vital substrates for building neuromorphic dynamic systems capable of information processing with high efficiency, are discussed. Typical examples of neuromorphic dynamic systems based on nonlinear memristors, including memristive reservoir, memristive oscillatory neural network, and memristive chaotic computing, are highlighted.
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.
A neuromorphic computing system may be able to learn and perform a task on its own by interacting with its surroundings. Combining such a chip with complementary metal–oxide–semiconductor ...(CMOS)‐based processors can potentially solve a variety of problems being faced by today's artificial intelligence (AI) systems. Although various architectures purely based on CMOS are designed to maximize the computing efficiency of AI‐based applications, the most fundamental operations including matrix multiplication and convolution heavily rely on the CMOS‐based multiply–accumulate units which are ultimately limited by the von Neumann bottleneck. Fortunately, many emerging memory devices can naturally perform vector matrix multiplication directly utilizing Ohm's law and Kirchhoff's law when an array of such devices is employed in a cross‐bar architecture. With certain dynamics, these devices can also be used either as synapses or neurons in a neuromorphic computing system. This paper discusses various emerging nanoscale electronic devices that can potentially reshape the computing paradigm in the near future.
Neuromorphic computing system takes its inspiration from the brain and it outperforms conventional computers (Von Neumann) in terms of energy consumption, reconfigurability, fault tolerance and scalability in many tasks that need human like thinking and learning. This article presents a timely review of various emerging nanoscale electronic devices that could potentially be used to realize such system on a hardware.
Memristive devices for computing Yang, J Joshua; Strukov, Dmitri B; Stewart, Duncan R
Nature nanotechnology,
01/2013, Letnik:
8, Številka:
1
Journal Article
Recenzirano
Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process ...information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.
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.
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