Energy efficient short-term memory characteristics in Ag/SnOx/TiN RRAM for neuromorphic system

Kwon, Oh‐Min; Shin, Jiwoong; Chung, Dae-Won; Kim, Sungjun

doi:10.1016/j.ceramint.2022.06.328

Cited by 28 publications

(14 citation statements)

References 41 publications

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“…[1,2] Most organic electronic devices share their working principles with those of biological synapses and neurons, that is ion migration, [3] and can effectively emulate synaptic and neuronal properties. [4][5][6][7] Moreover, memristors could be organized in large crossbar arrays to perform weighted vector matrix multiplication naturally by the electrical current summation. Being the most massively parallel operation in deep learning algorithms, it is very resource expensive for traditional von Neumann digital computing systems.…”

Section: Introductionmentioning

confidence: 99%

Combination of Organic‐Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

Мацукатова

Прудников

Kulagin

et al. 2023

Advanced Intelligent Systems

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Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle‐to‐cycle, c2c, or device‐to‐device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain‐inspired computing systems built with partially unreliable analog elements.

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Section: Introductionmentioning

confidence: 99%

Combination of Organic‐Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

Мацукатова

Прудников

Kulagin

et al. 2023

Advanced Intelligent Systems

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“…To date, many materials have been employed as a switching layer in the RS devices including metal oxides, [15][16][17][18] 2D materials, [19] nitrides, [20][21][22] biomaterials, [23][24][25][26] etc. In recent years, various research groups are using electrochemical deposition techniques for the fabrication of different RS devices.…”

Section: Introductionmentioning

confidence: 99%

Self‐Assembled Lanthanum Oxide Nanoflakes by Electrodeposition Technique for Resistive Switching Memory and Artificial Synaptic Devices

Patil

Kundale

Patil

et al. 2023

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In recent years, many metal oxides have been rigorously studied to be employed as solid electrolytes for resistive switching (RS) devices. Among these solid electrolytes, lanthanum oxide (La2O3) is comparatively less explored for RS applications. Given this, the present work focuses on the electrodeposition of La2O3 switching layers and the investigation of their RS properties for memory and neuromorphic computing applications. Initially, the electrodeposited La2O3 switching layers are thoroughly characterized by various analytical techniques. The electrochemical impedance spectroscopy (EIS) and Mott–Schottky techniques are probed to understand the in situ electrodeposition, RS mechanism, and n‐type semiconducting nature of the fabricated La2O3 switching layers. All the fabricated devices exhibit bipolar RS characteristics with excellent endurance and stable retention. Moreover, the device mimics the various bio‐synaptic properties such as potentiation‐depression, excitatory post‐synaptic currents, and paired‐pulse facilitation. It is demonstrated that the fabricated devices are non‐ideal memristors based on double‐valued charge‐flux characteristics. The switching variation of the device is studied using the Weibull distribution technique and modeled and predicted by the time series analysis technique. Based on electrical and EIS results, a possible filamentary‐based RS mechanism is suggested. The present results assert that La2O3 is a promising solid electrolyte for memory and brain‐inspired applications.

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“…14–16 Resistive memories fabricated using oxide materials such as SnO, ZnO, TiO 2 , CuO, AlON, Ta 2 O 5 , and SiO 2 have been verified to have superior nonvolatile memory characteristics. 2,3,17–27…”

Section: Introductionmentioning

confidence: 99%

“…1,2 Resistive memory with non-volatile rewritable characteristics have been widely reported in recent years, exhibiting the advantage of memory flexibility, which allows data to be written, read, and erased multiple times. 2,3 Moreover, they can be used to realize electronic synaptic devices. 4 In addition to its non-volatile rewritable memory characteristics, nonvolatile write-once-read-many-times (WORM), which shows special memory behavior for data storage, is also an 1important function of resistive memories.…”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16] Resistive memories fabricated using oxide materials such as SnO, ZnO, TiO 2 , CuO, AlON, Ta 2 O 5 , and SiO 2 have been verified to have superior nonvolatile memory characteristics. 2,3,[17][18][19][20][21][22][23][24][25][26][27] Strontium titanate (SrTiO 3 , STO) is an inorganic lead-free perovskite material, which possesses the benefits of non-toxic nature, wide bandgap, and high thermal stability. [28][29][30] Thus, it has been widely employed in solar cells, capacitors, and transistors.…”

Section: Introductionmentioning

confidence: 99%

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Effect of stoichiometry on the resistive switching characteristics of STO resistive memory

et al. 2023

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Energy efficient short-term memory characteristics in Ag/SnOx/TiN RRAM for neuromorphic system

Cited by 28 publications

References 41 publications

Combination of Organic‐Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

Combination of Organic‐Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

Self‐Assembled Lanthanum Oxide Nanoflakes by Electrodeposition Technique for Resistive Switching Memory and Artificial Synaptic Devices

Effect of stoichiometry on the resistive switching characteristics of STO resistive memory

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