Scalable oxide-based memcapacitive crossbar arrays for 1 Kb neuromorphic memory
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) memcapacitor crossbar arrays achieving device sizes as small as 10 µ m × 10 µ m using yttrium oxide (Y 2 O 3 ) 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 Y 2 O 3 -based crossbar arrays also display robust switching behavior, with a high on/off current ratio ( I RATIO > 150), excellent endurance (∼18 000) cycles, long retention ∼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 × 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.
Authors
- Shaibal Mukherjee (ORCID: https://orcid.org/0000-0002-9879-7278)
- Yu‐Lun Chueh (ORCID: https://orcid.org/0000-0002-0155-9987)
- Umesh V. Shembade
- Saurabh Yadav (ORCID: https://orcid.org/0000-0002-1255-0216)
- Tukaram D. Dongale (ORCID: https://orcid.org/0000-0003-2536-6132)
- Kasturi A. Rokade
- Lokesh Kumar Hindoliya
- Animesh Paul
- Mayank Dubey (ORCID: https://orcid.org/0000-0001-7536-5096)
Institutions
- Shivaji University (IN)
- Korea University (KR)
- National Tsing Hua University (TW)
- D.Y. Patil University (IN)
- Indian Institute of Technology Indore (IN)
Publication Details
- Journal
- Journal of Physics D Applied Physics
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1088/1361-6463/add8a0
- Citations
- 5
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 3.65