Nociceptor-Enhanced Spike-Timing-Dependent Plasticity in Memristor with Coexistence of Filamentary and Non-Filamentary Switching,Advanced Materials Technologies

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Nociceptor-Enhanced Spike-Timing-Dependent Plasticity in Memristor with Coexistence of Filamentary and Non-Filamentary Switching
Advanced Materials Technologies ( IF 6.4 ) Pub Date : 2024-05-19 , DOI: 10.1002/admt.202400440
Dongyeol Ju ₁ , Jungwoo Lee ₁ , Sungjun Kim ₁

Affiliation

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/TaO_x/TiO_x/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/TaO_x/TiO_x/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaO_x/TiO_x/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/TaO_x/TiO_x/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles.

中文翻译：

丝状和非丝状开关共存的忆阻器中伤害感受器增强的尖峰时间依赖性可塑性

在大数据时代，传统的计算架构由于处理单元和存储单元相互独立，在处理海量数据时面临着局限性，从而造成瓶颈和高能耗。受人脑信息交换机制的启发，神经形态计算提供了一种有前景的解决方案。电阻式随机存取存储器件，特别是那些具有双层结构（如 Pt/TaO _x /TiO _x /TiN）的器件，由于其简单的设计、低功耗以及与现有技术的兼容性，显示出神经形态计算的潜力。本研究研究了 Pt/TaO _x /TiO _x /TiN 器件在神经形态计算中的突触应用。 Pt/TaO _x /TiO _x /TiN器件中非丝状和丝状开关的独特共存使得能够实现储库计算以及人工伤害感受器和突触的功能。此外，基于损伤增强的尖峰时间依赖性可塑性范式检查了人工伤害感受器和突触之间的联系。这项研究强调了 Pt/TaO _x /TiO _x /TiN 器件在神经形态计算方面的潜力，为模拟伤害感受器、突触和学习原理提供了框架。

更新日期：2024-05-19

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