Impact of the La<sub>2</sub>NiO<sub>4+δ</sub> Oxygen Content on the Synaptic Properties of the TiN/La<sub>2</sub>NiO<sub>4+δ</sub>/Pt Memristive Devices

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/La 2 NiO 4+δ /Pt memristive devices based on thermally annealed La 2 NiO 4+δ films is thoroughly investigated. Using electron energy loss spectroscopy (EELS), it is shown that post‐deposition annealing using inert (Ar) or oxidizing (O 2 ) atmospheres affects the interstitial oxygen content (δ) in the La 2 NiO 4+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/La 2 NiO 4+δ /Pt device demonstrates filamentary‐like behavior, fast switching, and low energy consumption. On the other hand, the O 2 annealed TiN/La 2 NiO 4+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/La 2 NiO 4+δ /Pt devices for artificial synapse applications.

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Publication Details

Journal
Advanced Electronic Materials
Published
2024-09-26
DOI
https://doi.org/10.1002/aelm.202400096
Citations
4
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.83

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article

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

Carmen Jiménez, Aleksandra Koroleva, César Magén, Mónica Burriel et al.
4 citations
Advanced Electronic Materials
Advanced Memory and Neural Computing
0.83
article

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

Carmen Jiménez, Aleksandra Koroleva, César Magén, Mónica Burriel, Hervé Roussel, Thoai‐Khanh Khuu, Elena Ioana Vatajelu, Céline Ternon
article en
4 citations

Abstract

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/La 2 NiO 4+δ /Pt memristive devices based on thermally annealed La 2 NiO 4+δ films is thoroughly investigated. Using electron energy loss spectroscopy (EELS), it is shown that post‐deposition annealing using inert (Ar) or oxidizing (O 2 ) atmospheres affects the interstitial oxygen content (δ) in the La 2 NiO 4+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/La 2 NiO 4+δ /Pt device demonstrates filamentary‐like behavior, fast switching, and low energy consumption. On the other hand, the O 2 annealed TiN/La 2 NiO 4+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/La 2 NiO 4+δ /Pt devices for artificial synapse applications.

Advanced Electronic MaterialsVol. 10(11)
Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), Universidad de Zaragoza (ES), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), CEA Grenoble (FR), Instituto de Nanociencia y Materiales de Aragón (ES), Laboratoire d'Électronique des Technologies de l'Information (FR), Université Grenoble Alpes (FR)
Agence Nationale de la Recherche, Centre National de la Recherche Scientifique, Buddhist Tzu Chi Medical Foundation, Universidad de Zaragoza, Agencia Estatal de Investigación
Affordable and clean energy
Openalex Percentile: Top 27%
Advanced Memory and Neural Computing
0.83
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