Impact of the La2NiO4+δ Oxygen Content on the Synaptic Properties of the TiN/La2NiO4+δ/Pt Memristive Devices

IF 5.3 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Aleksandra Koroleva, Thoai-Khanh Khuu, César Magén, Hervé Roussel, Carmen Jiménez, Céline Ternon, Elena-Ioana Vatajelu, Mónica Burriel
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Abstract

The rapid development of brain-inspired computing requires new artificial components and architectures for its hardware implementation. In this regard, memristive devices emerged as potential candidates for artificial synapses because of their ability to emulate the plasticity of the biological synapses. In this work, the synaptic behavior of the TiN/La2NiO4+δ/Pt memristive devices based on thermally annealed La2NiO4+δ films is thoroughly investigated. Using electron energy loss spectroscopy (EELS), it is shown that post-deposition annealing using inert (Ar) or oxidizing (O2) atmospheres affects the interstitial oxygen content (δ) in the La2NiO4+δ films. Electrical characterization shows that both devices exhibit long-term potentiation/depression (LTP/LTD) and spike-timing-dependent plasticity (STDP). At the same time, the Ar annealed TiN/La2NiO4+δ/Pt device demonstrates filamentary-like behavior, fast switching, and low energy consumption. On the other hand, the O2 annealed TiN/La2NiO4+δ/Pt devices are forming-free, exhibiting interfacial-like resistive switching with slower kinetics. Finally, the simulation tools show that spiking neural network (SNN) architectures with weight updates based on the experimental data achieve high inference accuracy in the digit recognition task, which proves the potential of TiN/La2NiO4+δ/Pt devices for artificial synapse applications.

Abstract Image

La2NiO4+δ 氧含量对 TiN/La2NiO4+δ/Pt 膜电极突触特性的影响
脑启发计算的快速发展需要新的人工组件和架构来实现其硬件。在这方面,忆阻性器件因其能够模拟生物突触的可塑性而成为人工突触的潜在候选器件。在这项工作中,我们深入研究了基于热退火 La2NiO4+δ 薄膜的 TiN/La2NiO4+δ/Pt Memristive 器件的突触行为。使用电子能量损失光谱(EELS)显示,使用惰性(Ar)或氧化(O2)气氛进行沉积后退火会影响 La2NiO4+δ 薄膜中的间隙氧含量(δ)。电学特性分析表明,这两种器件都表现出长期延时/抑制(LTP/LTD)和尖峰计时可塑性(STDP)。同时,氩退火的 TiN/La2NiO4+δ/Pt 器件表现出丝状行为、快速开关和低能耗。另一方面,O2 退火的 TiN/La2NiO4+δ/Pt 器件没有成型,表现出类似界面的电阻开关,但动力学速度较慢。最后,模拟工具显示,基于实验数据进行权重更新的尖峰神经网络(SNN)架构在数字识别任务中实现了很高的推理准确性,这证明了 TiN/La2NiO4+δ/Pt 器件在人工突触应用方面的潜力。
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来源期刊
Advanced Electronic Materials
Advanced Electronic Materials NANOSCIENCE & NANOTECHNOLOGYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
11.00
自引率
3.20%
发文量
433
期刊介绍: Advanced Electronic Materials is an interdisciplinary forum for peer-reviewed, high-quality, high-impact research in the fields of materials science, physics, and engineering of electronic and magnetic materials. It includes research on physics and physical properties of electronic and magnetic materials, spintronics, electronics, device physics and engineering, micro- and nano-electromechanical systems, and organic electronics, in addition to fundamental research.
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