Self‐Rectifying Short‐Term Memory Phenomena Through Integration of TiOx Oxygen Reservoir and Al2O3 Barrier Layers for Neuromorphic System

神经形态工程学 期限(时间) 氧气 材料科学 光电子学 纳米技术 物理 计算机科学 人工神经网络 人工智能 量子力学
作者
Hyeonseung Ji,Sungjoon Kim,Sungjun Kim
出处
期刊:Advanced materials and technologies [Wiley]
被引量:1
标识
DOI:10.1002/admt.202400895
摘要

Abstract In this study, a tri‐layer Pt/Al/TiO x /HfO x /Al 2 O 3 /Pt memristor device is fabricated and analyze its electrical characteristics for reservoir computing and neuromorphic systems applications. This device incorporates an oxygen reservoir of a TiO x and a barrier layer of an Al 2 O 3 , enabling stable bipolar switching characteristics without the need for an electroforming process over 10 3 cycles. It also exhibits self‐rectifying properties under a negative bias. Based on these characteristics, it is investigated essential synaptic functions such as long‐term potentiation (LTP), long‐term depression (LTD), paired‐pulse facilitation (PPF), spike‐rate‐dependent plasticity (SRDP), spike‐duration‐dependent plasticity (SDDP), spike‐number‐dependent plasticity (SNDP), and spike‐amplitude‐dependent plasticity (SADP), to assess their suitability for neuromorphic applications that mimic biological synapses. Furthermore, utilizing the short‐term memory characteristics of the device, reservoir computing (RC) measurement from [0000] to [1111] in 4‐bit representation is conducted. This capability enables us to achieve a high accuracy of 95.5% in MNIST pattern recognition tasks. Lastly, the natural decay characteristics caused by oxygen ion migration in the device, examining the transition from short‐term to long‐term memory in image memorization tasks is explored. The potential for deployment in high‐density crossbar arrays by calculating the read margin based on the device I–V curve and programming scheme is also evaluated.
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