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Scalable oxide-based memcapacitive crossbar arrays for 1 Kb neuromorphic memory
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Scalable oxide-based memcapacitive crossbar arrays for 1 Kb neuromorphic memory

Animesh Paul, Saurabh Yadav, Kasturi A. Rokade, Umesh Shembade, Lokesh Kumar Hindoliya, Mayank Dubey, Tukaram D. Dongale, Yu-Lun Chueh 和 Shaibal Mukherjee
Journal of physics. D, Applied physics, 卷.58(23), 頁.235103
09/06/2025
Web of Science ID: WOS:001494414800001

摘要

Physical Sciences Physics Physics, Applied Science & Technology
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 x 32) memcapacitor crossbar arrays achieving device sizes as small as 10 mu m x 10 mu m using yttrium oxide (Y2O3) as the switching material, deposited via dual ion beam sputtering system. The arrays exhibit low C2C variability (1.01% for V-SET and 2.56% for V-RESET) and low D2D variability (1.70% for V-SET and 4.83% for V-RESET). The Y2O3-based crossbar arrays also display robust switching behavior, with a high on/off current ratio (I-RATIO > 150), excellent endurance (similar to 18 000) cycles, long retention similar to 160 000 s) and low power consumption of 17 pW. Electrochemical impedance spectroscopy 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 x 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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