Ultralow switching voltage slope based on two-dimensional materials for integrated memory and neuromorphic applications

Sun, Linfeng; Hwang, Genuwoo; Choi, Wooseon; Han, Gyeongtak; Zhang, Yishu; Jiang, Jinbao; Zheng, Shoujun; Watanabe, Kenji; Taniguchi, Takashi; Zhao, Mali; Zhao, Rong; Kim, Young Min; Yang, Heejun

doi:10.1016/j.nanoen.2020.104472

Cited by 57 publications

(46 citation statements)

References 32 publications

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“…A similar performance could be observed on subsequent pulses who were dependent on the previous resistance states. Apart from RRAM devices with traditional metal oxides, short-term synaptic performance of devices fabricated with 2D materials are also under investigation [159][160][161]. Sun et al reported their research on 2D-material-based devices with h-BN as a functional layer [159].…”

Section: Short-term Plasticity For Rram Devicesmentioning

confidence: 99%

“…Apart from RRAM devices with traditional metal oxides, short-term synaptic performance of devices fabricated with 2D materials are also under investigation [ 159 , 160 , 161 ]. Sun et al reported their research on 2D-material-based devices with h-BN as a functional layer [ 159 ]. As demonstrated in Figure 12 e, an obvious increase could be observed for ON-state current, which indicated the rise of synaptic strength due to the repeated pulse stimulation.…”

Section: Bionic Synaptic Applicationmentioning

confidence: 99%

“…( d ) Repeated STP response with the model fitting of TiO x -based RRAM device, reproduced from [ 158 ], with permission from Springer Nature, 2020. ( e ) Synaptic facilitation response to consecutive pulses of the device with h-BN, reproduced from [ 159 ], with permission from Elsevier, 2020.…”

Section: Figurementioning

confidence: 99%

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Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

et al. 2020

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Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

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Section: Short-term Plasticity For Rram Devicesmentioning

confidence: 99%

Section: Bionic Synaptic Applicationmentioning

confidence: 99%

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Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

et al. 2020

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“…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 ]…”

Section: Figurementioning

confidence: 99%

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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“…The number of reports on 2D materials‐based synaptic devices has accordingly increased rapidly. The most representative 2D materials in the aforementioned are graphene, transition metal dichalcogenides (TMDs) (e.g., MoS 2 and WSe 2 ), black phosphorus (BP), hexagonal boron nitride (h‐BN), and various vdW heterostructures with these 2D materials …”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Synaptic Devices Based on 2D Materials

Sun

Wang

Yang

2020

Advanced Intelligent Systems

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Diverse synaptic plasticity with a wide range of timescales in biological synapses plays an important role in memory, learning, and various signal processing with exceptionally low power consumption. Emulating biological synaptic functions by electric devices for neuromorphic computation has been considered as a way to overcome the traditional von Neumann architecture in which separated memory and information processing units require high power consumption for their functions. Synaptic devices are expected to conduct complex signal processing such as image classification, decision‐making, and pattern recognition in artificial neural networks. Among various materials and device architectures for synaptic devices, 2D materials and their van der Waals (vdW) heterostructures have been attracting tremendous attention from researchers based on their capacity to mimic unique synaptic plasticity for neuromorphic computing. Herein, the basic operations of biological synapses and physical properties of 2D materials are discussed, and then 2D materials and their vdW heterostructures for advanced synaptic operations with novel working mechanisms are reviewed. In particular, there is a focus on how to design synaptic devices with the vdW structures in terms of critical 2D materials and their limitations, providing insight into the emerging synaptic device systems and artificial neural networks with 2D materials.

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Ultralow switching voltage slope based on two-dimensional materials for integrated memory and neuromorphic applications

Cited by 57 publications

References 32 publications

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Recent Progress in Synaptic Devices Based on 2D Materials

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