Artificial Synapses Based on in-Plane Gate Organic Electrochemical Transistors

Qian, Chuan; Sun, Jia; Kong, Lingan; Gou, Guangyang; Yang, Junliang; He, Jun; Gao, Yongli; Wan, Qing

doi:10.1021/acsami.6b08866

Cited by 163 publications

(146 citation statements)

References 41 publications

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“…It should be noted here that short‐term synaptic plasticity can also be mimicked on ECTs with a relative low electrical field . Additionally, some ionotronic transistors exhibit both EDL and electrochemical operating mechanisms depending on the amplitudes of applied gate voltages . Thus, both short‐ and long‐term synaptic plasticity can be mimicked on such ionotronic transistors.…”

Section: Ionotronic Transistorsmentioning

confidence: 99%

“…[14,35,[59][60][61] Additionally, some ionotronic transistors exhibit both EDL and electrochemical operating mechanisms depending on the amplitudes of applied gate voltages. [36,[62][63][64] Thus, both short-and long-term synaptic plasticity can be mimicked on such ionotronic transistors. For instance, Yu et al [32,48] reported chitosan-gated ITO transistors.…”

Section: Synaptic Plasticity Mimicked On Ionotronic Transistorsmentioning

confidence: 99%

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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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Section: Ionotronic Transistorsmentioning

confidence: 99%

Section: Synaptic Plasticity Mimicked On Ionotronic Transistorsmentioning

confidence: 99%

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Zhu

2019

Physica Rapid Research Ltrs

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“…[6] However, these algorithms have proven to be computationally intensive, requiring >100 graphics processing such as poly (3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), due to the low voltages required to modulate the material's conductance. [32][33][34][35][36] When implemented in a three-terminal architecture as an NVRM, PEDOT:PSS devices show exceptionally low switching energies (10 pJ for a 100 µm 2 device) in addition to linear and symmetric conductance response required for "blind" weight update (i.e., not involving a read). [32][33][34][35][36] When implemented in a three-terminal architecture as an NVRM, PEDOT:PSS devices show exceptionally low switching energies (10 pJ for a 100 µm 2 device) in addition to linear and symmetric conductance response required for "blind" weight update (i.e., not involving a read).…”

Section: Introductionmentioning

confidence: 99%

Mechanisms for Enhanced State Retention and Stability in Redox‐Gated Organic Neuromorphic Devices

Keene

Melianas

Burgt

et al. 2018

Adv Elect Materials

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units (GPUs) and >1000 central processing units (CPUs) for some of the highest performance demonstrations. [4] In order to approach the massively parallel and energy efficient operation of the brain (≈25 W), [7] neuromorphic computing architectures have been proposed which utilize physical processes in materials to emulate synaptic behavior. [8][9][10] Among these, resistive memory (memristive) devices [11,12] have gained traction as a highly suitable option, offering projected efficiency gains of up to 10 6 over von Neumann architectures when implementing ANN algorithms. [13,14] Efficiency gains originate from parallel computation and scale with array size (N) [15] : an N × N sized neuromorphic array can simultaneously perform N 2 operations using Ohm's and Kirchoff's laws (multiply and accumulate, respectively), whereas GPUs can perform only N operations simultaneously.Nonvolatile memristive devices such as phase change memory (PCM) and resistive random-access memory (ReRAM) have been proposed for use as nonvolatile artificial synapses due to their ability to tune the resistance state by applied voltage pulses. [16][17][18] By arranging these devices in a crossbar array architecture, vector-matrix multiplication (VMM) can be performed in an analog fashion where the input vector is represented by voltages, the operator matrix is represented by the conductance of each memristive element, and the output vector is represented by currents. These crossbar arrays have recently been implemented in a dot-product engine which uses VMM operations to classify handwritten digits with ≈90% accuracy. [19] However, this demonstration requires a timeconsuming feedback programming scheme which reduces the parallelism of the write operation to the array and ultimately its overall efficiency.Recently, electrochemical nonvolatile redox memory (NVRM) devices [20,21] have emerged as an ideal candidate for neuromorphic arrays as they decouple read and write operations, thereby allowing for low-energy programming and accurately tunable conductance states while potentially avoiding the time-voltage dilemma. [22,23] Furthermore, organic materials are an attractive alternative to conventional resistive memory due to their linearly tunable conductance, [22] biocompatibility, [24] and unique switching mechanisms which significantly differ from their inorganic counterparts. [25][26][27][28][29][30] In particular, there has been interest in mixed ionic-electronic conductors, Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. One promising contender for efficient ANN implementation is crossbar arrays of resistive memory elements that emulate the synaptic strength between neurons within the ANN. Organic nonvolatile redox memory has recently been demonstrated as a promising device for neuromorphic computing, offering a continuous range of linearly programmable resistance states and tunable electronic and elect...

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“…P3HT has great potential for use in organic electrochemical transistors (OECTs). Artificial synapses based on ion‐gel in‐plane gated OECTs have been obtained . Some important synaptic functions were emulated, including STP, PPF, self‐tuning, spike‐logic operation, spatiotemporal dendritic integration, and modulation.…”

Section: Three‐terminal Synaptic Devicementioning

confidence: 99%

“…Organic synapses Small 2019, 15,1900695 can be freestanding [41] and stretchable; [67] these are important advantages that give these synapses a wide range of applications in wearable, skin-attachable, and implantable electronics. Therefore, numerous organic electronic synapses have been fabricated based on many kinds of organic materials, including pentacene, [68,69] poly(3-hexylthiophene) (P3HT), [70,71] polymer poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfate) (PEDOT:PSS), [72,73] and PEDOT doped with poly(tetrahydrofuran) (PEDOT:PTHF). [74] P3HT has great potential for use in organic electrochemical transistors (OECTs).…”

Section: Organic Electronic Synapsesmentioning

confidence: 99%

Recent Progress in Three‐Terminal Artificial Synapses: From Device to System

Han

Wei

et al. 2019

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256

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Synapses are essential to the transmission of nervous signals. Synaptic plasticity allows changes in synaptic strength that make a brain capable of learning from experience. During development of neuromorphic electronics, great efforts have been made to design and fabricate electronic devices that emulate synapses. Three‐terminal artificial synapses have the merits of concurrently transmitting signals and learning. Inorganic and organic electronic synapses have mimicked plasticity and learning. Optoelectronic synapses and photonic synapses have the prospective benefits of low electrical energy loss, high bandwidth, and mechanical robustness. These artificial synapses provide new opportunities for the development of neuromorphic systems that can use parallel processing to manipulate datasets in real time. Synaptic devices have also been used to build artificial sensory systems. Here, recent progress in the development and application of three‐terminal artificial synapses and artificial sensory systems is reviewed.

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Artificial Synapses Based on in-Plane Gate Organic Electrochemical Transistors

Cited by 163 publications

References 41 publications

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Mechanisms for Enhanced State Retention and Stability in Redox‐Gated Organic Neuromorphic Devices

Recent Progress in Three‐Terminal Artificial Synapses: From Device to System

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