Scalable Oxide-based Memcapacitive Crossbar Arrays for 1Kb Neuromorphic Memory

神经形态工程学 横杆开关 可扩展性 计算机科学 计算机体系结构 操作系统 电信 人工智能 人工神经网络
作者
Animesh Paul,Saurabh Yadav,Kasturi A. Rokade,Umesh V. Shembade,Lokesh Kumar Hindoliya,Mayank Dubey,Tukaram D. Dongale,Yu‐Lun Chueh,Shaibal Mukherjee
出处
期刊:Journal of Physics D [Institute of Physics]
标识
DOI:10.1088/1361-6463/add8a0
摘要

Abstract Memcapacitors are being investigated as potential candidates for high-density data storage. However, developing high-density memcapacitive devices for complex applications is challenging due to higher cycle-to-cycle (C2C) and device-to-device (D2D) variations. In this work, we demonstrate the fabrication of high-density (32 × 32) 1 Kb memcapacitor crossbar arrays achieving device sizes as small as 10 µm × 10 µm using yttrium oxide (Y2O3) as the switching material, deposited via Dual Ion Beam Sputtering (DIBS) system. The arrays exhibit low C2C variability (1.01% for VSET and 2.56% for VRESET) and low D2D variability (1.70% for VSET and 4.83% for VRESET). The Y2O3-based crossbar arrays also display robust switching behavior, with a high on/off current ratio (ION/OFF>150), excellent endurance (~18,000) cycles, long retention ~160,000 s) and low power consumption of 17 pW. Electrochemical impedance spectroscopy (EIS) has been utilized to examine the electrical behavior, providing insights into device performance. Neuromorphic functionalities are further demonstrated through Potentiation (learning) and Depression (forgetting) mechanisms. Moreover, a 16 × 16 array subset is employed to electrically encode random alphabet patterns and exhibit neuromorphic learning capabilities, underscoring the potential of these devices for analog and neuromorphic applications.
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