Structural Engineering of Li-Based Electronic Synapse for High Reliability

Choi, Yoo Seong; Lee, Chuljun; Kim, Myung-Jun; Song, Yubin; Hwang, Hyunsang; Lee, Daeseok

doi:10.1109/led.2019.2950202

Cited by 30 publications

(27 citation statements)

References 18 publications

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“…The conductance increases gradually under stimulation by positive voltage pulses, but when a negative voltage pulse is applied, lithium ions are released from the PSiO 2 layer to recombine with holes in the t 2g valence band of Li x CoO 2 . The conductance decreases gradually under the stimulation of negative voltage pulses 37 .…”

Section: Resultsmentioning

confidence: 97%

“…However, the nonuniform pore diameters in the silicon oxide layer cause large variations in the essential switching elements, and random and nonuniform ion migration in LiCoO 2 hampers the optimal switching functionalities. Lithium ion migration in LiCoO 2 -based memristors is similar to the information exchange process between synapses and neurons in the brain [36][37][38][39][40] . LiCoO 2 is used in Li-ion batteries to improve the capacity and mitigate the volume expansion during deintercalation of lithium ions under repetitive charging and discharging [41][42][43] .…”

Section: Introductionmentioning

confidence: 99%

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Artificial synapses with a sponge-like double-layer porous oxide memristor

et al. 2021

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Closely following the rapid development of artificial intelligence, studies of the human brain and neurobiology are focusing on the biological mechanisms of neurons and synapses. Herein, a memory system employing a nanoporous double-layer structure for simulation of synaptic functions is described. The sponge-like double-layer porous (SLDLP) oxide stack of Pt/porous LiCoO2/porous SiO2/Si is designed as presynaptic and postsynaptic membranes. This bionic structure exhibits high ON–OFF ratios up to 108 during the stability test, and data can be maintained for 105 s despite a small read voltage of 0.5 V. Typical synaptic functions, such as nonlinear transmission characteristics, spike-timing-dependent plasticity, and learning-experience behaviors, are achieved simultaneously with this device. Based on the hydrodynamic transport mechanism of water molecules in porous sponges and the principle of water storage, the synaptic behavior of the device is discussed. The SLDLP oxide memristor is very promising due to its excellent synaptic performance and potential in neuromorphic computing.

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Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Artificial synapses with a sponge-like double-layer porous oxide memristor

et al. 2021

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“…Positive current measured during negative bias is indicative of backpropagation of Li + ions, from the TiO 2 anode and the SiO x electrolyte towards the cathode because of the aforementioned EMF 22 . The non-zero crossing I-V characteristic, because of the Li + ion backpropagation (self-injection) 29 , denotes the relaxation of the conductance enhancement, highlighting the diffusive character of the devices.…”

Section: Electrical Characterizationmentioning

confidence: 99%

“…S3), providing a large enough window for calculations with millisecond pulses. It is noted that with proper material selection and engineering of the solid electrolyte properties, it is possible to drastically modify the relaxation dynamics 29 , thus enabling the optimization of the programming characteristics depending on the memory or synaptic behavior needed to be emulated.…”

Section: Analog Conductance Modulation Progressive Enhancement and Dmentioning

confidence: 99%

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Evidence of Biorealistic Synaptic Behavior in Diffusive Li-based Two-terminal Resistive Switching Devices

Ioannou

Kyriakides

Schneegans

et al. 2020

Sci Rep

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Following the recent advances in artificial synaptic devices and the renewed interest regarding artificial intelligence and neuromorphic computing, a new two-terminal resistive switching device, based on mobile Li+ ions is hereby explored. Emulation of neural functionalities in a biorealistic manner has been recently implemented through the use of synaptic devices with diffusive dynamics. Mimicking of the spontaneous synaptic weight relaxation of neuron cells, which is regulated by the concentration kinetics of positively charged ions like Ca2+, is facilitated through the conductance relaxation of such diffusive devices. Adopting a battery-like architecture, using LiCoO2 as a resistive switching cathode layer, SiOx as an electrolyte and TiO2 as an anode, Au/LiCoO2/SiOx/TiO2/p++-Si two-terminal devices have been fabricated. Analog conductance modulation, via voltage-driven regulation of Li+ ion concentration in the cathode and anode layers, along with current rectification and nanobattery effects are reported. Furthermore, evidence is provided for biorealistic synaptic behavior, manifested as paired pulse facilitation based on the summation of excitatory post-synaptic currents and spike-timing-dependent plasticity, which are governed by the Li+ ion concentration and its relaxation dynamics.

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Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

Byun

Choi

Kwon

et al. 2022

Adv Materials Technologies

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Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting considerable attention from academia and the industry. Although it is not completely successful yet, remarkable achievements have been reported pertaining to synaptic devices that can leverage NVM capable of storing multiple states. The analog synaptic devices performing computation similar to biological nerve systems are crucial in energy‐efficient analog neuromorphic computing systems. To use NVM as an analog synaptic device, researchers focus on improving device characteristics. Among various characteristics, the most challenging one is linearity and symmetry of synaptic weight update that is required for on‐chip training. In this regard, this review paper discusses recent synaptic device improvements focusing on novel schemes tailored for each NVM device to improve the linearity and symmetry. In addition to device‐level studies, recent research achievements are reviewed expanded up to chip‐level studies because in realizing neuromorphic hardware systems beyond a single synaptic device, several considerations and requirements are needed to confirm for high‐level design, and accordingly, cooptimize among synaptic devices, synapse arrays, electrical circuits, neural networks, algorithms, and implementation. Also, this review paper introduces various circuit and algorithmic approaches to compensate for the non‐ideality of the analog synaptic device.

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Structural Engineering of Li-Based Electronic Synapse for High Reliability

Cited by 30 publications

References 18 publications

Artificial synapses with a sponge-like double-layer porous oxide memristor

Artificial synapses with a sponge-like double-layer porous oxide memristor

Evidence of Biorealistic Synaptic Behavior in Diffusive Li-based Two-terminal Resistive Switching Devices

Recent Advances in Synaptic Nonvolatile Memory Devices and Compensating Architectural and Algorithmic Methods Toward Fully Integrated Neuromorphic Chips

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