Synaptic electronics: materials, devices and applications

Kuzum, Duygu; Yu, Shimeng; Wong, Hang

doi:10.1088/0957-4484/24/38/382001

Cited by 1,067 publications

(889 citation statements)

References 123 publications

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“…Because of the ability to continuously change the resistance in response to the input electrical signal (by analogy with a synapse), the memristive devices are considered as a basis of the electronic neuromorphic systems designed using the principles of biological neural structures [7][8][9][10]. It has already been shown that memristive nanostructures can mimic such a key function of the biological synapse as synaptic plasticity, the shortterm and long-term facilitation/depression and spiketiming-dependent plasticity (STDP).…”

Section: Introductionmentioning

confidence: 99%

Field‐ and irradiation‐induced phenomena in memristive nanomaterials

Mikhaylov

Gryaznov

Belov

et al. 2016

Phys. Status Solidi C

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The breakthrough in electronics and information technology is anticipated by the development of emerging memory and logic devices, artificial neural networks and brain-inspired systems on the basis of memristive nano-materials represented, in a particular case, by a simple 'metal-insulator-metal' (MIM) thin-film structure. The present article is focused on the comparative analysis of MIM devices based on oxides with dominating ionic (ZrOx, HfOx) and covalent (SiOx, GeOx) bonding of various composition and geometry deposited by magnetron sputtering. The studied memristive devices demonstrate reproducible change in their resistance (resistive switching - RS) originated from the formation and rupture of conductive pathways (filaments) in oxide films due to the electric-field-driven migration of oxygen vacancies and/or mobile oxygen ions. It is shown that, for both ionic and covalent oxides under study, the RS behaviour depends only weakly on the oxide film composition and thickness, device geometry (down to a device size of about 20x20 mu m(2)). The devices under study are found to be tolerant to ion irradiation that reproduces the effect of extreme fluences of high-energy protons and fast neutrons. This common behaviour of RS is explained by the localized nature of the redox processes in a nanoscale switching oxide volume. Adaptive (synaptic) change of resistive states of memristive devices is demonstrated under the action of single or repeated electrical pulses, as well as in a simple model of coupled (synchronized) neuron-like generators. It is concluded that the noise-induced phenomena cannot be neglected in the consideration of a memristive device as a nonlinear system. The dynamic response of a memristive device to periodic signals of complex waveform can be predicted and tailored from the viewpoint of stochastic resonance concept. (C) 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

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Section: Introductionmentioning

confidence: 99%

Field‐ and irradiation‐induced phenomena in memristive nanomaterials

Mikhaylov

Gryaznov

Belov

et al. 2016

Phys. Status Solidi C

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“…Previous XPS studies of CeO x suggest that the Ce 3d XPS core-level spectrum has three-lobed envelops (which are located at around 882-890 eV, 895-910 eV and 916 eV) that originate from the different final states of mixed valency [1][2][3]. In Ce 3d spectrum, the u''' (v '''), u'' (v'') and u (v) denote the Ce 4+ final states and correspond to Ce 3d 9 4f 0 O 2p 6 , Ce 3d 9 4f 1 O 2p 5 and Ce 3d 9 4f 4 O 2p 4 respectively for Ce3d 3/2 and Ce3d 5/2 . The Ce 3+ final states in Ce 3d spectrum correspond to 3d 9 4f 1 O 2p 5 and 3d 9 4f 2 O 2p 4 and are labeled as u' (v') and u o (v o ) in the XPS spectrum.…”

Section: X-ray Photoelectron Spectroscopy (Xps) Studiesmentioning

confidence: 99%

“…In general, memristors offer non-linear switching characteristics, and materials and process compatibility with advanced silicon manufacturing. These attributes have spurred the exploration of memristors as synaptic devices for realizing spike-based hardware learning systems that are capable of processing unstructured, temporal data [5][6][7][8][9][10]. However, for memristor-based technologies to be viable, the device should exhibit several key characteristics.…”

mentioning

confidence: 99%

A sub-1-volt analog metal oxide memristive-based synaptic device with large conductance change for energy-efficient spike-based computing systems

Hsieh

Roy

Chang

et al. 2016

Applied Physics Letters

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A memristor is a two-terminal 'memory resistor' electronic device, in which a metal oxide switching layer is sandwiched between two metal electrodes [1][2][3][4]. In general, memristors offer non-linear switching characteristics, and materials and process compatibility with advanced silicon manufacturing. These attributes have spurred the exploration of memristors as synaptic devices for realizing spike-based hardware learning systems that are capable of processing unstructured, temporal data [5][6][7][8][9][10]. However, for memristor-based technologies to be viable, the device should exhibit several key characteristics. It should have a compact nanoscale footprint, operate at a voltage close to 1V that is compatible with complementary metal oxide semiconductor (CMOS) technology, have reproducible electrical characteristics, and possess high switching speed to minimize the energy consumption [11]. Furthermore, the hardware integration of synaptic connections in advanced neural networks requires memristors with multiple resistive states [12,13]. These are challenging requirements and are difficult to implement without significant innovations.The phenomenological principle of memristor device operation is based on the change in the physical properties of a conductive filament (associated with the presence of oxygen vacancies) by applying an electric field across the metal oxide switching layer [14][15][16]. The resulting motion of the oxygen vacancies alters the device resistance between low (Set) and high (Reset) states, depending on the direction and the amplitude of the electric field. So far, a variety of structures from a large set of materials (various metal oxide switching layers and metal electrodes) have been studied in the literature [4,17,18]. Several key findings can be drawn from those studies regarding the performance, energy and scalability of this type of devices. The most important finding reveals the trade-off between the switching energy and the data retention time-that is often referred to as voltage-time dilemma [19]. This trade-off is associated with the energy barrier of the device structure. For example, devices made of metal oxides with small energy bandgap (E g ), such as titanium oxide (TiO x , E g~3 .4eV), generally exhibit low operating voltage and compromised data retention [20], while those with large bandgap, such as hafnium oxide (HfO x , E g~5 .4eV) demonstrate the opposite [21]. However, the fabrication of devices with bilayer switching stacks has shown to be effective in mitigating this trade-off. In particular, the improvement in data retention was obtained by the incorporation of an ultra-thin wide bandgap metal oxide capping layer (for example aluminum oxide) [22]. On the other hand, the addition of a reactive capping metal (for example titanium, hafnium, etc.) as an oxygen scavenging layer provided a pathway for reducing the operating voltage of the devices [23,24]. Despite significant advances, a sub-1V memristive device that simultaneously affords built-in analog behavior...

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“…Brain-inspired computing is an emerging field, which can achieve artificial intelligence by means of simulating neural network of human's brain, as it can extend the capabilities of information technology beyond the Von Neumann paradigm [1,2]. In biological systems, synapses are the bridges between neurons [1,3], and they can change their intensity to enhance (synaptic potentiation) or weaken (synaptic depression) the connection between two neurons.…”

Section: Introductionmentioning

confidence: 99%

Simulation of the Spiking Neural Network based on Practical Memristor

et al. 2018

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Abstract. In order to gain a better understanding of the brain and explore biologically-inspired computation, significant attention is being paid to research into the spike-based neural computation. Spiking neural network (SNN), which is inspired by the understanding of observed biological structure, has been increasingly applied to pattern recognition task. In this work, a single layer SNN architecture based on the characteristics of spiking timing dependent plasticity (STDP) in accordance with the actual test of the device data has been proposed. The device data is derived from the Ag/GeSe/TiN fabricated memristor. The network has been tested on the MNIST dataset, and the classification accuracy attains 90.2%. Furthermore, the impact of device instability on the SNN performance has been discussed, which can propose guidelines for fabricating memristors used for SNN architecture based on STDP characteristics.

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Synaptic electronics: materials, devices and applications

Cited by 1,067 publications

References 123 publications

Field‐ and irradiation‐induced phenomena in memristive nanomaterials

Field‐ and irradiation‐induced phenomena in memristive nanomaterials

A sub-1-volt analog metal oxide memristive-based synaptic device with large conductance change for energy-efficient spike-based computing systems

Simulation of the Spiking Neural Network based on Practical Memristor

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