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相关概念视频

Non-ohmic Devices00:51

Non-ohmic Devices

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In most substances, the current flow is proportional to the voltage applied to it. A simple relationship between the values of current, voltage, and resistance is known as Ohm's law. Nonohmic devices do not exhibit a linear relationship between voltage and current. One such device is the semiconducting circuit element known as a diode. A diode is a circuit device that allows current flow in only one direction.
Consider a simple circuit consisting of a battery, a diode, and a resistor. A...
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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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一步化配置的Hf0.2Zr0.8O2 对于生物启发的CSNN神经形态计算的Memristive-Antiferroelectric设备

Jinhao Zhang1,2, Kangli Xu3, Chen Lu3

  • 1Shandong Key Laboratory of Next-Generation Semiconductor Technology and Systems, School of Integrated Circuits, Shandong University, Jinan 250100, China.

Nano letters
|December 4, 2025
PubMed
概括

这项研究引入了一个新的CMOS兼容Hf0.2Zr0.8O2平台用于混合神经形态计算. 它可以实现双模式的人工神经元和突触设备,在卷积尖端神经网络中实现高精度.

关键词:
兼容性 CMOS 的兼容性反铁电装置是一种反铁电装置.卷积尖端神经网络的神经网络.纪念馆是为了纪念.神经形态计算是一种神经形态计算.

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In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
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A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy
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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:

  • 传统的计算架构由于分离的组件而面临限制,阻碍了高效的神经形态系统的发展.
  • 开发兼容的人工神经元和突触设备对于推进混合神经形态计算至关重要.
  • 现有的解决方案往往缺乏CMOS兼容性,限制了与标准半导体制造工艺的集成.

研究的目的:

  • 介绍一个单,CMOS兼容的Hf0.2Zr0.8O2平台,用于制造记忆突触和抗铁电神经元设备.
  • 为了证明神经形态应用的TiN/Hf0.2Zr0.8O2/TiN堆的双模式功能.
  • 使用这些集成设备构建和评估一个卷积尖端神经网络.

主要方法:

  • 使用CMOS兼容的工艺制造一个单TiN/Hf0.2Zr0.8O2/TiN结构.
  • 使用已沉积的Hf0.2Zr0.8O2膜作为模拟导电调节的记忆突触.
  • 执行一阶段的沉积后炼,将相同的堆转化为呈现自发脱极化的抗铁电神经元装置.
  • 将双模式设备集成到一个卷积尖端神经网络架构中.

主要成果:

  • Hf0.2Zr0.8O2平台成功地从相同的材料堆中产生了具有记忆性的突触和抗铁电神经元功能.
  • 记忆器件展示了适合卷积特征提取的模拟导电量调制.
  • 反铁电装置表现出自发的去极化,使得基于尖峰的编码和生物学上可信的神经元动力学.
  • 集成卷积尖端神经网络在动态手势识别中实现了97.9%的高精度.

结论:

  • 展示的CMOS兼容的双模式设备平台为混合神经形态计算提供了统一的材料解决方案.
  • 这种方法通过利用流程兼容的制造来促进创建紧的神经形态硬件.
  • 在卷积尖端神经网络中成功实施突出了使用这些神经形态电子的先进AI应用程序的潜力.