Artificial Neural Network Classification Using Al-Doped HfOx-Based Ferroelectric Tunneling Junction with Self-Rectifying Behaviors
- Authors
- Lim, Eunjin; Ju, Dongyeol; Lee, Jungwoo; Park, Yongjin; Kim, Min-Hwi; Kim, Sungjun
- Issue Date
- May-2024
- Publisher
- American Chemical Society
- Citation
- ACS Materials Letters, v.6, no.6, pp 2320 - 2328
- Pages
- 9
- Journal Title
- ACS Materials Letters
- Volume
- 6
- Number
- 6
- Start Page
- 2320
- End Page
- 2328
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/74182
- DOI
- 10.1021/acsmaterialslett.3c01587
- ISSN
- 2639-4979
2639-4979
- Abstract
- In this study, we meticulously engineered an Al-doped hafnia-based ferroelectric tunneling junction (FTJ) with a metal-ferroelectric-silicon (MFS) structure. We conducted a thorough analysis of its memory characteristics, revealing a substantial remnant polarization of 24.17 μC/cm2, a noteworthy tunneling electroresistance value of 265, exceptional endurance with 106 operational cycles, and robust retention (>104 s), thereby demonstrating the viability of the FTJ as a nonvolatile memory device. Additionally, through rectification of this MFS FTJ, an effective array scale of approximately 1349 with a modified read scheme was ensured. Expanding our study of neuromorphic applications, we explored phenomena such as potentiation/depression, paired-pulse facilitation (PPF), excitatory postsynaptic currents (EPSC), and spike-rate-dependent plasticity (SRDP). Notably, this memristor has outstanding potential for visual memory processing. In conclusion, our findings unequivocally underscore the immense potential of the hafnia-based FTJ for applications in neural networks, emphasizing its significance in advancing neuromorphic computing. © 2024 American Chemical Society.
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