双终端MoS2记忆器和同质集成与一个MoS2晶体管用于神经网络
Shuai Fu1, Ji-Hoon Park2, Hongyan Gao1
1Department of Electrical Computer and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States.
Nano letters
|June 20, 2023
概括
研究人员开发了新的MoS2记忆器,具有类似晶体管的机制,可以无地集成到可编程神经网络中. 这种均质的集成使高效,可扩展的神经形态计算具有高模式识别准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 计算机工程 计算机工程
背景情况:
- 记忆器是神经网络的关键组件,但由于与晶体管不同的机制,它们面临着整合挑战.
- 记忆元和定位晶体管之间的缩放不匹配妨碍了神经形态系统的高效集成.
研究的目的:
- 为了展示一个基于电荷的机制与MoS2晶体管同质集成的MoS2记忆器.
- 为可编程神经形态网络创建一个晶体管-一个记忆器 (1T1M) 细胞.
主要方法:
- 使用基于充电的工作原理制造双终端MoS2记忆器.
- 在1T1M细胞中同质集成MoS2记忆器和MoS2晶体管.
- 这些集成电池的2x2网络阵列的实施和测试.
- 模拟神经网络使用现实的设备参数来评估可扩展性和性能.
主要成果:
- 成功展示了类似晶体管的基于电荷的操作的MoS2记忆器.
- 实现了memristors和晶体管的均集成到可定位的1T1M细胞中.
- 验证了2x2数组中集成细胞的可编程性和可定址性.
- 模拟神经网络实现了超过91%的模式识别准确度.
结论:
- MoS2 记忆元和晶体管的均质集成为扩展神经网络硬件不匹配提供了一个可行的解决方案.
- 开发的1T1M细胞适合组装可扩展,可编程的神经形态网络.
- 本文所介绍的机制和策略适用于其他用于记忆系统工程的半导体设备.
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