用于神经形态计算的TiN/Ti/HfO记忆器件:从突触可塑性到随机共振
David Maldonado1, Antonio Cantudo1, Eduardo Perez2,3
1Departamento de Electronica y Tecnologia de Computadores, Facultad de Ciencias, Universidad de Granada, Granada, Spain.
Frontiers in neuroscience
|October 5, 2023
概括
我们开发了TiN/Ti/HfO2/TiN记忆器,模仿生物突触进行神经形态计算. 这些设备展示了依赖于尖峰时间的可塑性和随机共振,这对于人工智能应用至关重要.
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.
- 计算机科学 计算机科学
背景情况:
- 神经形态计算旨在使用人工系统复制大脑功能.
- 记忆器件为构建节能和高性能神经形态硬件提供了有前途的特性.
- 了解和建模突触可塑性是开发智能学习系统的关键.
研究的目的:
- 用于神经形态应用的TiN/Ti/HfO2/TiN记忆器件的特征.
- 模拟和分析突触可塑性特征,特别是尖峰时间依赖可塑性 (STDP).
- 调查设备变化和随机共振对网络性能的影响.
主要方法:
- TiN/Ti/HfO2/TiN记忆装置的制造和电气特性.
- 实验测量和分析建模尖峰时间依赖可塑性 (STDP).
- 实施STDP模型作为一个学习规则在一个尖端神经网络 (SNN) 的MNIST数据集识别.
- 对SNN准确度的设备可变性和随机共振效应的分析.
主要成果:
- TiN/Ti/HfO2/TiN设备表现出模仿生物突触的行为,包括STDP.
- 一个模型准确地复制了测量的STDP数据.
- 基于STDP的SNN实现了对MNIST数据集的认可,变化影响了准确性.
- 随机共振被确定为一个重要的突触特征,高度依赖于噪声特征.
结论:
- 由于它们的突触可塑性,TiN/Ti/HfO2/TiN记忆器件适合用于神经形态计算.
- 开发的STDP模型及其集成到SNN中显示了学习和识别任务的潜力.
- 设备变性和随机共振是强大的神经形态系统设计需要考虑的关键因素.
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