All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing

Yang, Chuansen; Shang, Dashan; Liu, Nan; Fuller, Elliot James; Agrawal, Sapan; Talin, A. Alec; Li, Yong‐Qing; Shen, Baogen; Sun, Young

doi:10.1002/adfm.201804170

Cited by 383 publications

(399 citation statements)

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“…[108][109][110][111] In addition, electrochemical random-access memory (ECRAM) based on ion intercalation has recently been reported as a promising synaptic cell, showing multi-states and incremental switching with near-ideal switching symmetry and linearity. [29,[112][113][114][115][116][117][118][119] The electrochemically driven ion intercalation process is more controllable than filament-related ion movements in RRAM; therefore, ECRAM also exhibits a much smaller stochasticity. In addition, by borrowing the battery concept, those devices successfully decouple the read and write operations and thus realize low programming energy and long retention time simultaneously.…”

Section: Artificial Synapsesmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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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.

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Section: Artificial Synapsesmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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“…www.advelectronicmat.de be utilized to mimic LTP with forgetting effect, [159,[161][162][163] STP to LTP transformation, [157,160,[164][165][166][167][168][169][170] metaplasticitry, [112,[171][172][173][174][175] and triplet-STDP. [176][177][178] The device structure, size, switching performance, and emulated synaptic plasticity of the representative memristive devices are summarized in Table 1.…”

Section: Memristive Synapsesmentioning

confidence: 99%

“…three-terminal device, [168] and the transformation from STP to LTP can be achieved by increasing the stimuli amplitude and duration (see Figure 11d). …”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

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Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

186

127

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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“…[23][24][25] Among all IST-based synapse devices, Li + ion-based synapse transistor (LIST) shows better synaptic property because of high reversibility and ultra-high stability of Li + ion under electrolyte gating. [27,28] Polymer-based solid-state electrolyte systems give better control over OCP and stability, but patterning of top gate architecture with PEO: LiClO 4 electrolyte is challenging since they are soluble in a commonly used lithographic solvent. More recently, lithium phosphorus oxynitride (LiPON) was proposed as a solid electrolyte for the synaptic device.…”

Section: Introductionmentioning

confidence: 99%

Controlled Ionic Tunneling in Lithium Nanoionic Synaptic Transistor through Atomically Thin Graphene Layer for Neuromorphic Computing

Nikam

Kwak

Lee

et al. 2019

Adv Elect Materials

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computers due to the dense neural network of synapses. [3,4] Developing a highspeed and a low-cost artificial synapse device to build an artificial neural network to simulate the human brain like functionalities is the dominant scientific goal of the twenty-first century. Colossal exertion has been committed to developing a synapse device that can trigger the synaptic plasticity and nonvolatility. The conventional neural prototype chip was fabricated based on traditional complementary metal-oxide-semiconductor (CMOS) circuits. [5] However, consumption of high power due to a large number of transistors in COMS circuit limits its further development. [6,7] Besides, a different type of two-terminal memristor such as oxidebased resistive random-access memory (RRAM), [8] ferroelectric memory, [9][10][11][12] and phase-change memory (PCM) [13][14][15] has been employed to mimic the synaptic activity due to reversible analog switching. However, reduced control over conductance change and asynchronous read/write operation limit the application of twoterminal devices as a synaptic element. [16] More recently, a nanoionics synaptic transistor (IST) has been extensively studied and proposed as a suitable candidate for artificial synapses. [17][18][19][20] An IST consists of ionically conducting gate electrolyte and insulating or semiconducting channel. An IST relies on field-driven ion migration from an electrolyte to channel, thereby changing its doping state and hence its conductivity. This device shows reversibility near analog switching, nonvolatility, and low switching energy because of a filamentforming free switching. Additionally, these devices give a high level of accuracy to emulate the synaptic functionalities because synaptic weight update modulated by the gate terminal while the read operation performed on a channel.Recently, several reports show the synaptic behavior by adding and extracting the oxygen (O 2− ) or proton (H + ) in a channel from the electrolyte layer. [17,19,21,22] Both oxygen and proton-based synaptic transistors show unstable behavior and non-linearity due to the structural deformation of a channel as well as the electrolyte layer by mobile H + and O 2− ion. The advantage of using Li + ion in IST is due to more excellent Lithium nanoionic transistors have recently emerged as promising artificial synaptic devices for neuromorphic hardware systems. However, mimicking the essential synaptic functionalities including nonvolatile conductance modulation with a near-linear analog weight update has been a crucial milestone in those synaptic devices and has a direct impact on pattern recognition accuracy. The volatile channel conductance change due to the instability of the solid electrolyte interface and lithium-ion nucleation at electrolyte-channel interface are two key phenomena responsible for the nonlinear switching in lithium nanoionics transistor. Graphene is proposed as an atomically thin ionic tunneling layer to establish nonvolatile analog multilevel conduction in lithium nanoionic transisto...

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All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing

Cited by 383 publications

References 54 publications

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Memristive Synapses and Neurons for Bioinspired Computing

Controlled Ionic Tunneling in Lithium Nanoionic Synaptic Transistor through Atomically Thin Graphene Layer for Neuromorphic Computing

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