极性依赖的双模式AlN嵌入式RRAM,具有改进的随机切换和突触调制,用于神经形态计算
Jaewoo Choi1, Hyogeun Park1, Yongjin Byun1
1Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea.
The Journal of chemical physics
|October 23, 2025
概括
这项研究介绍了一种能够实现数字和模拟切换的新型电阻随机访问存储器设备. 这种双模式功能显示了节能神经形态计算应用的前景.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 电阻随机存储器 (ReRAM) 设备对于下一代计算至关重要.
- 在单个ReRAM单元中实现数字和模拟切换是神经形态应用的关键挑战.
研究的目的:
- 开发一个单一的ReRAM单元,具有极性依赖的双模式切换功能.
- 为了展示该设备在节能神经形态计算方面的潜力.
主要方法:
- 制造具有特定层次结构的Pt/Al/TaOx/AlN/Al2O3/Pt ReRAM设备.
- 在正和负偏差条件下对设备切换行为的描述.
- 评估模拟切换性能使用增量步骤脉冲与验证算法.
主要成果:
- 该设备表现出极性依赖的双模式切换:在正偏差下突然的数字切换和在负偏差下逐渐的模拟切换.
- 集成的AlN层充当电流限制器,使其能够实现自我合规和受控的光纤形成.
- 该设备实现了对模拟切换的6位分辨率,>10,000s保留,并模拟了突触可塑性.
- 使用设备数据的多层感知器在MNIST数据集上实现了93.5%的准确性.
结论:
- 开发的ReRAM设备为节能,硬件级神经形态计算提供了一个有前途的平台.
- 双模式切换和突触模拟能力是人工智能硬件的重大进步.
- 该设备的架构确保了稳定性和可靠性,抑制了当前的超速.
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