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灵活的单极IGZO基于晶体管的整合和火神经元用于尖端神经形态应用程序
IEEE transactions on biomedical circuits and systems
|October 2, 2023
概括
研究人员使用无形InGaZnO薄膜晶体管开发了可适应的Axon-Hillock电路,用于神经网络. 这些电路具有可调节的燃烧特性和低功耗,使环境相互作用具有生物学相关的时间表.
科学领域:
- 材料科学与工程 材料科学与工程
- 神经科学是一个神经科学.
- 电气工程 电气工程
背景情况:
- 尖端神经网络 (SNN) 对于模仿生物大脑功能至关重要.
- 阿克森 - 希洛克电路是SNN的基本组成部分,调节神经元发射.
- 现有的实施方案往往缺乏适应性和能源效率.
研究的目的:
- 使用新型薄膜晶体管技术设计和制造可适应的Axon-Hillock电路.
- 探索神经元属性的适应性,如峰值频率和宽度.
- 为了展示功能,如值电压适应和耐火周期调制.
主要方法:
- 在柔性聚胺基板上使用单极无极InGaZnO薄膜晶体管制造Axon-Hillock电路.
- 直接材料沉积和图案制作技术.
- 电路属性的表征,包括发射频率,尖峰宽度,值电压和功耗.
主要成果:
- 在输出尖峰频率和宽度方面表现出一致的适应性.
- 实现了值电压适应,输入信号差异化和耐火周期调制.
- 在单个电压输入时,燃烧频率呈现出三级变化.
- 报告的超低的平均功耗在nW范围内,每次峰值的能量低 (≈700 pJ在30 Hz).
结论:
- 开发的Axon-Hillock电路为尖端神经元模型提供了显著的适应性.
- 该技术可以在生物学上相关的时间尺度 (毫秒到秒) 上进行操作.
- 低功耗和高适应性为可扩展的SNN为环境交互和学习铺平了道路.
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