Capacitive neural network with neuro-transistors

Wang, Zhongrui; Rao, Mingyi; Han, Jin Woo; Zhang, Jiaming; Lin, Peng; Li, Yuning; Li, Can; Song, Wenhao; Asapu, Shiva; Midya, Rivu; Ye, Zhuo; Jiang, Hao; Yoon, Jung Ho; Upadhyay, Navnidhi K.; Joshi, Saumil; Hu, Miao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Wu, Huaqiang; Qiu, Qinru; Williams, R. Stanley; Xia, Qiangfei; Yang, J. Joshua

doi:10.1038/s41467-018-05677-5

Cited by 228 publications

(157 citation statements)

References 62 publications

(66 reference statements)

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“…[84,85] A SiO x RRAM device can generate controlled voltage transients, which resemble spike-like responses. [41,96,97] Importantly, these devices relax back to their highly resistive off state spontaneously (without the need of RESET operation) after firing, closely resembling the repolarization process of biological [75] Copyright 2016, Springer Nature. Metal filament-based CBRAM-like devices (diffusive memristors) have also been proved feasible to achieve the integrate-and-fire function.…”

Section: Artificial Neuronsmentioning

confidence: 97%

“…Wang et al integrated the Ag diffusive memristors onto the gate of a transistor, which then becomes the active front end of an "axon" for a stochastic LIF neuron emulator (Figure 7b). [41,96] In addition, both ferroelectric field-effect transistors (FeFETs) relying on ferroelectric polarization switching and MRAM devices relying on magnetization switching have also been employed to emulate biological neuron behaviors. Input signals are integrated by the memristor neuron that mimics dendritic spatial and temporal summation.…”

Section: Artificial Neuronsmentioning

confidence: 99%

“…[96,161,162] More fundamental studies at the materials and devices levels are needed to achieve more capable artificial intelligent components and systems. This is because www.advmat.de www.advancedsciencenews.com even a single biological neuron or synapse contains a number of complicated sub-components, such as different types of ion channels, and all of them collaboratively contribute to the neuromorphic functions.…”

Section: Memory Switching In Artificial Synapsesmentioning

confidence: 99%

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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 Neuronsmentioning

confidence: 97%

Section: Artificial Neuronsmentioning

confidence: 99%

Section: Memory Switching In Artificial Synapsesmentioning

confidence: 99%

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

et al. 2019

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“…In the past few years, ionotronic transistors or memristors were proposed to mimic advanced neural functions. By connecting pressure sensors and oscilloscope with neuromorphic devices and adoping mathematical modeling and software simulation, advanced neural functions are simulated, including logic function, image memorization, pattern recognition, face recognition, classic conditioning, tactile‐perception system, etc. In this section, we shortly discuss these achievements.…”

Section: Advanced Neural Functions Based On Ionotronic Neuromorphic Dmentioning

confidence: 99%

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Zhu

2019

Physica Rapid Research Ltrs

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The von Neumann bottleneck constrains developments of conventional artificial intelligences (AIs) toward portable, energy efficient, and truly brain‐like systems. Biological synapses connecting neurons deliver neural action potentials by regulating neurotransmitters between presynaptic and postsynaptic membranes. In a similar way, ionotronic transistors and memristors transmit electronic signals through the migration of ionic species in dielectric and resistive switching electrolyte under external electrical field. Thus, utilizing ionotronic devices to emulate synapses and building bionic neural networks opens up a brand‐new path to realize hardware‐based AI. Moreover, it would allow the fabrication of an artificial perception learning system to mimic the human perception system with multi‐sensory learning abilities. This review presents ionotronic devices for neuromorphic engineering applications. The operation mechanisms and brain‐inspired synaptic responses and neural functions are discussed. At last, outlooks for ionotronic devices to construct bionic neural networks are provided.

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“…Electron. It is noteworthy that some LIF neurons with tunable integration time have been realized using memcapacitors [223,224] or pseudo-memcapacitors (a diffusive memristive device with a series capacitor) [225] to replace normal capacitors. 2019, 5,1900287 its threshold, and soon, the capacitor performs discharging through the memristive device (DL loop in Figure 18b) and induces the plunge of the voltage across the memristive device resulting in it recovering to high resistance.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

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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Capacitive neural network with neuro-transistors

Cited by 228 publications

References 62 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

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Memristive Synapses and Neurons for Bioinspired Computing

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