Multi-gate memristive synapses realized with the lateral heterostructure of 2D WSe<sub>2</sub> and WO<sub>3</sub>

He, Huan; Yang, Rui; Huang, Heming; Yang, Fan-Fan; Wu, Yazhou; Shaibo, Jamal; Guo, Xin

doi:10.1039/c9nr07941f

Cited by 62 publications

(47 citation statements)

References 57 publications

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“…In doing so, this RRAM device has the capacity to mimic ionic mechanisms in synapses faithfully that enables efficient learning and data processing. [18][19][20][21][22][23][24] We also present an empirically-driven conduction-based model of our device.…”

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confidence: 99%

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Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Lin

Chen

et al. 2020

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The biological brain has set a golden standard in computational efficiency, both due to its massive parallelism, and its ability to perform in-memory computing within the same ionic substrate. Ion-gated channels determine synaptic strength which is known to be a mechanism for memory storage, and the very same ions that pass through these gates encode data in the form of spikes. Communication, computation, and storage all occur within the same local medium. The brain's ability to perform in-memory processing within a unified ionic mechanism has driven many researchers to apply ion-driven non-volatile memories to emulate learning rules at the device-level. [1-12] The operating principles of resistive random access memory (RRAM) draw parallel with biological synapses. From a physical standpoint, the top electrode (TE) corresponds to a pre-synaptic terminal, the insulator layer acts as the substrate through which neurotransmitters are released, and the bottom electrode (BE) Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.

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confidence: 99%

“…To test device reliability, the LiSiO x devices were subjected to high-temperature retention experiments. Various resistive Reproduced with permission, [20] Copyright 1998 Society for Neuroscience. d) The TiN electrode acts as the pre-synaptic terminal; the Pt electrode is the post-synaptic terminal.…”

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confidence: 99%

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Lin

Chen

et al. 2020

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“…The partial oxidation of 2D semiconductors is another strategy of building memristor-type artificial synapses by integrating the merits of layered materials and the corresponding oxides in one device. [114,232,234,253,254] In 2015, Bessonov et al first prepared a flexible memristor based on a vertical MoO x /MoS 2 heterostructure by a solution-based method followed by thermal oxidation. [232] The MoS 2 layer functioned as a mechanical support, while the MoO x layer provided memristive properties.…”

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confidence: 99%

“…[ 234 ] Besides the vertical heterostructure formed by partial oxidation, lateral heterostructures such as WSe 2 –WO 3 have also been fabricated as memristive synapses with gate‐tunable memristive properties. [ 254 ] In contrast to the case of 2D semiconductor‐based heterostructures, a native oxide layer can easily form on metallic 2D materials such as NbS 2 without the need for thermal oxidation to afford a NbO x /NbS 2 heterostructure perfectly suited for use as both a lateral and vertical memristive device with the assistance of the conducting NbS 2 . [ 114 ]…”

Section: Bioelectronic Devices Based On 2d Materials Beyond Graphenementioning

confidence: 99%

Bioelectronics‐Related 2D Materials Beyond Graphene: Fundamentals, Properties, and Applications

Wang

Sun

Ding

et al. 2020

Adv Funct Materials

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The rapid development of bioelectronics has lead to the emergence of versatile biorelated architectures for information processing systems, sensors, actuators, and wearable devices. In recent years, 2D materials beyond graphene have been demonstrated to be essential bioelectronic device components owing to their merits of atomic thickness, high transparency, large specific area, unique electrical/optical properties, and good biocompatibility. Herein, the recent advances in the bioelectronic applications of 2D materials beyond graphene are reviewed and their physicochemical properties, biocompatibility, synthesis, and processing methods are described. Moreover, the design strategies, working mechanisms, and performances of various bioelectronic devices based on these materials are summarized and the perspectives and challenges of this research field are highlighted. Thus, this review is expected to inspire new insights and technical advances for future research.

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“…speed (<1 ns), [12] efficient energy consumption (<1 pJ), [13] great scalability (2 nm), [14] and versatile performances (analog/digital and volatile/nonvolatile switching), memristive devices, the resistance of which can be modulated by electrical stimuli, are promising for ANNs. Memristive devices have been realized in various materials, such as oxides, [15][16][17][18] polymers, [19,20] 2D materials, [21][22][23][24][25] and perovskite materials. [26,27] In addition, the vector-matrix multiplication (VMM) operation, the most dataintensive computation in ANNs, can be conducted in a memristive crossbar array.…”

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confidence: 99%

Artificial Neural Networks Based on Memristive Devices: From Device to System

Huang

Wang

et al. 2020

Advanced Intelligent Systems

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Thanks to the advance in transistor scaling, computing capabilities of central processing units (CPUs) and graphics processing units (GPUs) have been increased greatly, resulting in the fast development of artificial neural networks (ANNs). ANNs predict the correlation between a set of input and output parameters by emulating the principles of biological neural networks. [1] Nowadays, ANNs have been applied to a broad spectrum, such as computer vision, natural language processing, autonomous driving, and decision making. Although ANNs are advantageous in some specific tasks, such as playing the game of Go, [2,3] the human brain outperforms ANNs in cognitive and classification tasks with fast speed and high power efficiency. The advantages of the human brain lie in the special architectures with high massive parallelism, spiking neurons, and enormous number of basic computing units of neurons (%10 11) and synapses (%10 15), whereas computers show high speed of basic operation and precision. [4] The massively parallel processing compensates the low speed of neurons and synapses, whereas spiking neurons compute more powerful than other types of neurons. [5] The parallel processing occurs based on variable synaptic weights, which is the basis of memory. Moreover, the same information is processed by many neurons and transmitted to a downstream neuron, which enhances the precision as well. Therefore, the human brain shows a salient feature, i.e., in-memory computing, suitable for high-performance applications with a large amount of data. In contrast, conventional digital computing systems face the Von Neumann bottleneck, because in the Von Neumann architecture, computing units and memory are separated, hindering the enhancement of ANNs. Thus, several novel architectures are promoted, such as brain-inspired computing (processing information based on spiking neurons, also called as spiking neural networks [SNNs]) and in-memory computing on chip, to overcome the Von Neumann bottleneck. [6] To implement the new architectures, artificial synapses and neurons are necessary. The massive parallelism of the brain takes advantage of a large number of neurons and synapses; however, the emulation of the functions of biological synapses and neurons requires tens of complement metal-oxide-semiconductor (CMOS) transistors. Therefore, it is urgent to realize synapses and neurons with a simplified method for the development of ANNs. [7] As synapses adjust their weights step-by-step repeatedly, synaptic devices need to exhibit analog switching behavior. [8] However, biological neurons show the analog membrane potential and generate the all-or-nothing rapidly, so both analog and digital devices are acceptable for the emulation of neurons. Many emerging devices have been proposed to develop ANNs based on hardware, e.g., memristive devices, phase change memories (PCMs), [9] magnetic random-access memories (MRAMs), [10] and ferroelectric field-effect transistors (FeFETs). [11] Each technology has its advantages and disadvantages. PCM and M...

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Multi-gate memristive synapses realized with the lateral heterostructure of 2D WSe₂ and WO₃

Abstract: Multi-gate memristive synapses based on the lateral heterostructure of 2D WSe2 and WO3 are demonstrated for the first time.

Cited by 62 publications

References 57 publications

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Bioelectronics‐Related 2D Materials Beyond Graphene: Fundamentals, Properties, and Applications

Artificial Neural Networks Based on Memristive Devices: From Device to System

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Multi-gate memristive synapses realized with the lateral heterostructure of 2D WSe2 and WO3

Abstract: Multi-gate memristive synapses based on the lateral heterostructure of 2D WSe2 and WO3 are demonstrated for the first time.

Cited by 62 publications

References 57 publications

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Bioelectronics‐Related 2D Materials Beyond Graphene: Fundamentals, Properties, and Applications

Artificial Neural Networks Based on Memristive Devices: From Device to System

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Multi-gate memristive synapses realized with the lateral heterostructure of 2D WSe₂ and WO₃