Nanoionics-Based Three-Terminal Synaptic Device Using Zinc Oxide

Pillai, Premlal Balakrishna; Souza, M.M. De

doi:10.1021/acsami.6b13746

Cited by 139 publications

(98 citation statements)

References 54 publications

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“…These results are enabled by the short‐term memory properties of our devices. The long‐term memory is limited to the retention in Figure c, which is comparable to prior work in the order of hours . Another important property of a synaptic device is the dependence of the output signal on the time interval between pulses.…”

Section: Resultsmentioning

confidence: 55%

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Ferroelectric‐Like Charge Trapping Thin‐Film Transistors and Their Evaluation as Memories and Synaptic Devices

Daus

Lenarczyk

Petti

et al. 2017

Adv Elect Materials

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This work presents a defect charging mechanism in 5‐nm‐thick amorphous Al2O3 thin‐films fabricated on plastic, which leads to multistate memory effects, and thus the realization of synaptic thin‐film transistors (TFTs) for neuromorphic applications. First, the Al2O3 thin‐films are characterized in metal–insulator–metal stacks. These devices exhibit ferroelectric‐like behavior, which is visible in the small‐signal capacitance and the surface charge density. Furthermore, the quantum‐mechanical simulation of the current–voltage characteristic leads to a physical model with trap charges close to the anode interface where deep‐level traps are identified by fitting the experimentally obtained resonant tunneling peaks. The trap charge lifetime and frequency behavior is evaluated in InGaZnO4 TFTs, where the 5‐nm‐thick Al2O3 layer is employed as gate dielectric. At an operating voltage as low as ±2 V, a charge trapping retention up to ≈3 h and a discernable ON/OFF read‐out with a factor >3 at 2 kHz are achieved. When subjected to a train of gate–source voltage pulses, the TFTs show charge integration properties which emulate facilitating and depressing behaviors of biological synapses. These results indicate that thin low‐temperature defect‐rich metal‐oxide dielectrics may be candidates for low‐voltage memory applications and neuromorphic circuits on unconventional substrates.

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Section: Resultsmentioning

confidence: 55%

“…Thus, we investigated the synaptic functionality of the TFTs in more detail. This is typically done by monitoring the device response on a train of voltage pulses . Similarly to previous work, we show in Figure the behavior of a facilitating (a) and depressing (b) synapse.…”

Section: Resultsmentioning

confidence: 88%

Ferroelectric‐Like Charge Trapping Thin‐Film Transistors and Their Evaluation as Memories and Synaptic Devices

Daus

Lenarczyk

Petti

et al. 2017

Adv Elect Materials

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“…[8] Thus, the building a novel computational architecture based on artificial synapses to achieve neural neuromorphic computing is considered a promising approach.In previous reports, artificial synapses have been successfully proposed and implemented in various ways. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Artificial synapses exhibit potential for superior applications and have stimulated more and more relevant research. In view of the fact that visual perception is the way in which humans obtain more than 80% of their external information, [26] the importance of storing and processing acquired visual signals is evident; in spite of this, the optoelectronic synapse, [27][28][29] which is a promising candidate for processing visual signals, is still in an initial stage of research.All-inorganic perovskite materials [30][31][32] have attracted wide attention in the academic field due to their narrow half-peak width, high photoluminescence quantum yield, easy regulation of the luminescence wavelength, and good thermal stability.…”

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confidence: 99%

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Zhu

et al. 2020

Adv Funct Materials

161

129

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Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.

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“…[5] However, consumption of high power due to a large number of transistors in COMS circuit limits its further development. [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. However, reduced control over conductance change and asynchronous read/write operation limit the application of twoterminal devices as a synaptic element.…”

mentioning

confidence: 99%

“…[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. [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.…”

mentioning

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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Nanoionics-Based Three-Terminal Synaptic Device Using Zinc Oxide

Cited by 139 publications

References 54 publications

Ferroelectric‐Like Charge Trapping Thin‐Film Transistors and Their Evaluation as Memories and Synaptic Devices

Ferroelectric‐Like Charge Trapping Thin‐Film Transistors and Their Evaluation as Memories and Synaptic Devices

Optoelectronic Perovskite Synapses for Neuromorphic Computing

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

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