Flexible Artificial Synaptic Devices Based on Collagen from Fish Protein with Spike‐Timing‐Dependent Plasticity

Raeis‐Hosseini, Niloufar; Park, Youngjun; Lee, Jang‐Sik

doi:10.1002/adfm.201800553

Cited by 139 publications

(120 citation statements)

References 82 publications

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“…Further, assuming that the electron trap states are limited to a single energy level (E trap ), the current in the Child's law region as per space charge limited conduction theory can be expressed as I = (9/8) [13,29,31] Here, A is the effective area of the device, µ is the mobility, and ε and d are the permittivity and thickness of the oxide layer, respectively. In addition, V is the applied voltage, E c is the energy at the conduction band minima, N c is the effective density of states in the conduction band at temperature T, N t is the total trap density, and k is the Boltzmann's constant.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

“…In neuron science, the advanced rules for competitive Hebbian learning are generally defined by spike-timing-dependent plasticity, which in fact refers to the change of the synaptic weight with relative dynamically and temporally intercoupling activities between the pre-and postsynaptic spikes (Δt pre-post ). [14,23,24,31] In fact, for spatiotemporal biological processes, when the spike signals arrive simultaneously on the preand post-neurons, the resultant connecting strength between neurons depends on the relative timing delay between them. To mimic the STDP behavior, two identical voltage spikes (+1.0 V; Δd: 1 ms) with specified time interval (Δt pre-post ) were applied to the bottom Al and top Ag electrodes as pre-and postsynaptic neurons.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

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Vertically Aligned WS₂ Layers for High‐Performing Memristors and Artificial Synapses

Kumar

Ban

Kim

et al. 2019

Adv Elect Materials

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functionalities could be mimicked by using and fine tuning (by spikes) the conductance of two-terminal memristive devices. [4,6,7] Therefore, a broad range of materials, for instance, metal oxides, organic/inorganic perovskites, 2D layered materials, etc., have been used to build memristive devices, which were further employed as artificial synapses. [8][9][10][11] Despite the achievements made so far, the immediate development in particular for metal-oxide-based artificial synapses faces several major problems: a great degree of variability (both from device to device and cycle to cycle) and trivial range of linearly programmable conductance states. [12] All these obstacles must be resolved to implement error-free neuromorphic functioning. These objectives could be achieved by either exploring new material architecture or improving the performance of existing ones with a detailed understanding of fundamental change dynamics.The realization of memristive properties in metal oxide hinges on the distribution of well-known intrinsic defects or ions, mainly oxygen vacancies (O V ). [10,11,13,14] Generally, under the influence of applied electric field, local oxygen vacancy density distribution changes and thus modifies the total (two-terminal) resistance of the device. [6,15,16] Therefore, to realize the reproducible (cycle-to-cycle) and stable (device-to-device) performance, it is essential to have a better control on the oxygen vacancy/ion distribution and its movement with applied field. As a matter of fact, researchers have made several attempts to confine the oxygen ion dynamics along the preferential sites. For instance, the insertion of metal nanodots or nanoparticles, and embedded nanotip electrodes have been found to be effective in improving the cycle-to-cycle uniformity. [17][18][19] However, large size and random distribution of metal nanoparticles generate hindrance to realize reproducible performance from device to device over a larger area. Indeed, the key challenges to design metal-oxidebased artificial synapse are to have reproducible and robust (against electric pulses) performance along with large number of linearly programmable states, which is yet to be achieved.As a promising strategy, the insertion of 2D layered materials into memristive device structure offers a new possibility to improve the performance. In this scenario, few attempts have been made; however, most of them used planar configurations, which occupy relatively large space and are difficult to stack in 3D Inspired by the human brain, the quest for high-performing neuromorphic architecture has recently gained more attention, which can be achieved by two-terminal memristors. However, due to random and uncontrolled filament formation during a typical switching process, conventional memristors suffer from severe shortcomings such as temporal/spatial reproducibility as well as trivial sensitivity against applied spikes, however all these properties are crucial for accurate and quick information processing. Here, reproducible and robust...

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Section: Wwwadvelectronicmatdementioning

confidence: 99%

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Vertically Aligned WS₂ Layers for High‐Performing Memristors and Artificial Synapses

Kumar

Ban

Kim

et al. 2019

Adv Elect Materials

View full text Add to dashboard Cite

show abstract

“…Moreover, biocompatible, biodegradable materials are used in wearable and implantable electronics. They have potential applications in flexible neuromorphic platforms, such as albumen, human hair keratin, chitosan, fish collagen, etc. As an important branch for neuromorphic engineering, flexible neuromorphic device may find new applications, including artificial afferent nerve, tactile perceptual learning system, etc.…”

Section: Introductionmentioning

confidence: 99%

Restickable Oxide Neuromorphic Transistors with Spike‐Timing‐Dependent Plasticity and Pavlovian Associative Learning Activities

Zhu

Xiao

et al. 2018

Adv Funct Materials

150

149

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In neural system, both spike-timing-dependent plasticity (STDP) and conditioned reflex are vital synaptic learning mechanisms to regulate advanced neural activities, being associated with temporally coupled stimuli. Thus, realization of STDP and conditioned reflex on a single solid-state device may open up new opportunities for neuromorphic engineering. Here, restickable chitosan-gated oxide neuromorphic transistors are fabricated on polyimide tape. Based on protonic electrochemical doping/de-doping processes at indium-tin-oxide/chitosan interfaces, four types of STDP learning rules are successfully demonstrated, including Hebbian STDP, anti-Hebbian STDP, symmetrical STDP, and visual STDP. Trained with Hebbian STDP, Pavlovian associative learning and extinction behaviors are demonstrated successfully on a single-oxide neuromorphic transistor. No complex device circuits are needed. It is interesting to note here that the devices can be pasted on different holders with different curvature radii without degrading the transistor performances and STDP learning rules. Moreover, the proposed oxide neuromorphic transistors can be dissolved in deionized water easily. The results here indicate potential applications of the proposed restickable oxide neuromorphic transistors in flexible neuromorphic cognitive platforms.

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“…Thus, electrical conductive states will change either in the channel of transistors or in the resistant switch layer of memristors . Interestingly, this unique ionotronic phenomenon exists in biocompatible and environment friendly materials including egg albumen, carrageenan, chitosan, starch, fish collagen, etc. Thus, ionotronic neuromorphic devices adopting such materials as functional layer may have potential applications in biocompatible hardware‐based AI.…”

Section: Introductionmentioning

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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Flexible Artificial Synaptic Devices Based on Collagen from Fish Protein with Spike‐Timing‐Dependent Plasticity

Cited by 139 publications

References 82 publications

Vertically Aligned WS₂ Layers for High‐Performing Memristors and Artificial Synapses

Vertically Aligned WS₂ Layers for High‐Performing Memristors and Artificial Synapses

Restickable Oxide Neuromorphic Transistors with Spike‐Timing‐Dependent Plasticity and Pavlovian Associative Learning Activities

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

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Flexible Artificial Synaptic Devices Based on Collagen from Fish Protein with Spike‐Timing‐Dependent Plasticity

Cited by 139 publications

References 82 publications

Vertically Aligned WS2 Layers for High‐Performing Memristors and Artificial Synapses

Vertically Aligned WS2 Layers for High‐Performing Memristors and Artificial Synapses

Restickable Oxide Neuromorphic Transistors with Spike‐Timing‐Dependent Plasticity and Pavlovian Associative Learning Activities

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

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Vertically Aligned WS₂ Layers for High‐Performing Memristors and Artificial Synapses

Vertically Aligned WS₂ Layers for High‐Performing Memristors and Artificial Synapses