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Carbon Nanotube-Based Flexible Ferroelectric Synaptic Transistors for Neuromorphic Computing
ACS Applied Materials & Interfaces ( IF 9.5 ) Pub Date : 2022-06-23 , DOI: 10.1021/acsami.2c07825
Fan Xia 1, 2 , Tian Xia 1, 2 , Li Xiang 1, 3 , Sujuan Ding 4, 5 , Shuo Li 1 , Yucheng Yin 6 , Meiqi Xi 1 , Chuanhong Jin 4, 5 , Xuelei Liang 1 , Youfan Hu 1, 2
Affiliation  

Biological nervous systems evolved in nature have marvelous information processing capacities, which have great reference value for modern information technologies. To expand the function of electronic devices with applications in smart health monitoring and treatment, wearable energy-efficient computing, neuroprosthetics, etc., flexible artificial synapses for neuromorphic computing will play a crucial role. Here, carbon nanotube-based ferroelectric synaptic transistors are realized on ultrathin flexible substrates via a low-temperature approach not exceeding 90 °C to grow ferroelectric dielectrics in which the single-pulse, paired-pulse, and repetitive-pulse responses testify to well-mimicked plasticity in artificial synapses. The long-term potentiation and long-term depression processes in the device demonstrate a dynamic range as large as 2000×, and 360 distinguishable conductance states are achieved with a weight increase/decrease nonlinearity of no more than 1 by applying stepped identical pulses. The stability of the device is verified by the almost unchanged performance after the device is kept in ambient conditions without additional passivation for 240 days. An artificial neural network-based simulation is conducted to benchmark the hardware performance of the neuromorphic devices in which a pattern recognition accuracy of 95.24% is achieved.

中文翻译:

用于神经形态计算的基于碳纳米管的柔性铁电突触晶体管

自然界进化而来的生物神经系统具有惊人的信息处理能力,对现代信息技术具有很大的参考价值。为了扩展电子设备在智能健康监测和治疗、可穿戴节能计算、神经假肢等方面的功能,用于神经形态计算的柔性人工突触将发挥关键作用。在这里,基于碳纳米管的铁电突触晶体管通过不超过 90°C 的低温方法在超薄柔性基板上实现,以生长铁电电介质,其中单脉冲、双脉冲和重复脉冲响应证明了良好-模拟人工突触的可塑性。器件中的长期增强和长期抑制过程表现出高达 2000 倍的动态范围,通过施加阶梯式相同脉冲,实现了 360 个可区分的电导状态,重量增加/减少非线性不超过 1。器件在环境条件下放置 240 天后性能几乎没有变化,验证了器件的稳定性。进行了基于人工神经网络的模拟,以对神经形态设备的硬件性能进行基准测试,其中模式识别准确率达到 95.24%。器件在环境条件下放置 240 天后性能几乎没有变化,验证了器件的稳定性。进行了基于人工神经网络的模拟,以对神经形态设备的硬件性能进行基准测试,其中模式识别准确率达到 95.24%。器件在环境条件下放置 240 天后性能几乎没有变化,验证了器件的稳定性。进行了基于人工神经网络的模拟,以对神经形态设备的硬件性能进行基准测试,其中模式识别准确率达到 95.24%。
更新日期:2022-06-23
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