高性能神经形态计算和逻辑操作基于自组装的垂直对齐的纳米复合材料SrTiO3:MgO薄膜记忆器
Zhenqiang Guo1, Gongjie Liu1, Yong Sun1
1Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China.
ACS nano
|October 28, 2023
概括
使用SrTiO3:MgO膜的稳定记忆器可以实现高效的神经形态计算. 这些设备在图像识别和强大的逻辑内存功能方面表现出高精度,克服了传统架构的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 神经科学是一个神经科学.
- 计算机工程 计算机工程
背景情况:
- 神经形态计算,利用memristors进行内存计算,为·诺伊曼架构的能量和效率限制提供了解决方案.
- 现有的memristors遭受不稳定和非线性电导更新,阻碍识别准确性和神经网络硬件的集成.
研究的目的:
- 为先进的神经形态计算应用开发一种高度稳定的memristor.
- 为了应对不稳定的导电性更新的挑战,并提高基于memristor的神经网络的识别准确性.
主要方法:
- 自组装的垂直对齐纳米复合材料 (VAN) SrTiO:MgO薄膜的制造.
- 电阻开关属性的表征,包括电压变化和导电率线性.
- 模拟突触行为,并使用横条内核演示卷积图像处理.
主要成果:
- 该SrTiO:MgO记忆电阻器表现出极好的电阻切换,设置/重置电压的变化率低 (4.7%/-5.6%) 和高导电率线性 (非线性 = 0.34).
- 一个人工神经网络实现了高识别准确度97.50%的手写数字.
- 在各种噪音条件下 (Poisson,盐和胡,高斯式) 保持了高的识别准确度,并实验证了非挥发性逻辑记忆功能.
结论:
- 开发的VAN SrTiO:MgO记忆器为高性能神经形态计算提供了一个有前途的材料系统.
- 设备设计克服了关键的局限性,实现了稳定的突触行为,准确的图像识别和可靠的逻辑操作.
- 这项工作为将先进的memristor技术集成到未来的计算系统中提供了可行的途径.
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