Transition of short-term to long-term memory of Cu/TaOx/CNT conductive bridge random access memory for neuromorphic engineering

Kim, Jihyung; Choi, Jin Hyeong; Kim, Sunghun; Choi, Changsoon; Kim, Sungjun

doi:10.1016/j.carbon.2023.118438

Cited by 6 publications

(1 citation statement)

References 51 publications

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“…Figure d illustrates the PPF synaptic function, which demonstrates the changes in the intensities of two synaptic stimuli over time at different intervals (Figure S12). − As the interval time increases, the magnitude of current decay increases, which is analogous to the STM effect. Various intervals ranging from 10 μs to 1 s are added between two identical programming pulses with a pulse amplitude of 2.5 V and pulse width of 7 μs.…”

Section: Resultsmentioning

confidence: 94%

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Lee,

Huang,

Chang

et al. 2024

ACS Nano

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In this study, we investigate the coexistence of short-and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS 2 / Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS 2 in a crossbar structure. The presence of both short-and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short-and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spikerate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at highconductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.

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Section: Resultsmentioning

confidence: 94%

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Lee,

Huang,

Chang

et al. 2024

ACS Nano

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Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching

Ju,

Lee,

Kim

2024

Adv Materials Technologies

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In the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high‐energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low‐power consumption, and compatibility with existing technology. This study investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury‐enhanced spike‐time‐dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles.

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Self-aligned TiOx-based 3D vertical memristor for a high-density synaptic array

Lee,

Kim,

Kim

2024

Front. Phys.

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Transition of short-term to long-term memory of Cu/TaOx/CNT conductive bridge random access memory for neuromorphic engineering

Cited by 6 publications

References 51 publications

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching

Self-aligned TiOx-based 3D vertical memristor for a high-density synaptic array

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Transition of short-term to long-term memory of Cu/TaOx/CNT conductive bridge random access memory for neuromorphic engineering

Cited by 6 publications

References 51 publications

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching

Self-aligned TiOx-based 3D vertical memristor for a high-density synaptic array

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Product

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Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems

Programmable Retention Characteristics in MoS₂-Based Atomristors for Neuromorphic and Reservoir Computing Systems