상세 보기
- Lim, Eunjin;
- Ju, Dongyeol;
- Lee, Jungwoo;
- Park, Yongjin;
- Kim, Min-Hwi;
- 외 1명
WEB OF SCIENCE
8SCOPUS
9초록
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.
키워드
- 제목
- Artificial Neural Network Classification Using Al-Doped HfOx-Based Ferroelectric Tunneling Junction with Self-Rectifying Behaviors
- 저자
- Lim, Eunjin; Ju, Dongyeol; Lee, Jungwoo; Park, Yongjin; Kim, Min-Hwi; Kim, Sungjun
- 발행일
- 2024-05
- 유형
- Article
- 저널명
- ACS Materials Letters
- 권
- 6
- 호
- 6
- 페이지
- 2320 ~ 2328