在神经形态计算中具有高性能的非形态BN基础的突触设备
Juyeong Pyo1, Junwon Jang1, Dongyeol Ju1
1Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea.
Materials (Basel, Switzerland)
|October 28, 2023
概括
本研究探讨无形化 (BN) 用于神经形态计算. 制造的Pt/BN/TiN设备具有突触特征,为先进的人工智能硬件铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 神经科学是一个神经科学.
背景情况:
- ·诺伊曼架构由于处理器和内存间隔而面临性能限制.
- 六角化 (BN) 属性在电阻切换内存方面已经得到了很好的研究,但无形BN在内存应用中仍未得到充分研究.
- 神经形态系统需要高效的记忆元件来模仿生物突触.
研究的目的:
- 研究无形化 (BN) 在电阻开关记忆器件中的潜力.
- 为神经形态系统中的突触应用制造和描述一个Pt/BN/TiN装置.
- 为了优化强化和抑郁特征,模仿生物突触功能.
主要方法:
- 使用无形化制造Pt/BN/TiN装置.
- 通过电流-电压 (I-V) 曲线分析,对电阻开关机制的研究.
- 使用设备特征优化潜能和抑郁,以模拟突触可塑性.
- 通过控制导电度水平和时间间隔来评估短期记忆特征.
主要成果:
- 该Pt/BN/TiN设备显示了电阻切换行为.
- 成功模仿了突触特征,包括强化和抑郁.
- 实现了线性导电性更新,这对于人工神经网络的高识别率至关重要.
- 观察到的短期记忆效应是可控的.
结论:
- 无形化是一种有前途的材料,用于开发具有突触功能的memristor设备.
- 制造的Pt/BN/TiN设备显示出在神经形态计算和人工神经网络中使用的潜力.
- 对无形BN的进一步研究可能会导致下一代内存技术的重大进步.
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