电流/电压双模混合离子电子氧化物状晶体管用于神经形态计算
Wei Sheng Wang1,2, Xin Huang1, You Jie Huang1
1School of Physical Science and Technology, Ningbo University, Ningbo 315211, Zhejiang, P. R. China.
ACS applied materials & interfaces
|June 27, 2025
概括
一种新的混合离子氧化氧化晶体管 (HIODT) 能够实现双模电流/电压控制,用于神经形态计算. 该设备在模式识别方面实现了高精度,并模拟了生物疼痛感知,从而推进了人工智能硬件.
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:
- 电压驱动的神经形态设备解决了·诺伊曼瓶,但电流驱动的方法在实现突触功能方面面临挑战.
- 高效的神经形态计算需要能够模仿复杂的神经行为和学习的设备.
- 在人工设备中模仿生物感官系统,如疼痛感知,为先进的AI提供了新的途径.
研究的目的:
- 提出和描述一个电流/电压双模混合离子电离子氧化物状晶体管 (HIODT).
- 证明HIODT在执行基本突触功能和学习行为方面的能力.
- 探索该设备在模式识别和模拟生物感官功能的潜力.
主要方法:
- HIODT的制造和电气特性.
- 实施电流和电压峰值方案,用于突触重量更新和关联学习.
- 在三层人工神经网络中测试设备的性能,用于数字和时尚-MNIST识别.
- 仿真疼痛感知感受器 (PPN) 行为,如敏感化和脱敏化.
主要成果:
- HIODT表现出良好的电性能和丰富的离子动态.
- 有效的线性突触重量更新和关联性学习是通过双模态调制实现的.
- 对于小数字的识别准确度>90%,对于时尚-MNIST的识别准确度为~80%.
- 疼痛感知受体的关键特征被成功模拟.
结论:
- 拟议的HIODT为电流/电压双模态神经形态计算提供了一个有前途的平台.
- 该设备执行复杂学习和模拟生物功能的能力为先进的人工智能开辟了新的可能性.
- 这项工作为下一代功能神经形态器件的双模态增高策略提供了宝贵的见解.
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