Liu Ke, Wang Yongchang, Sun Miao, Lu Jiajia, Shi Deli, Xie Yanbo
School of Physical Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an 710072, China.
School of Aeronautics and Institute of Extreme Mechanics, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an 710072, China.
Nano Lett. 2025 Apr 23;25(16):6530-6538. doi: 10.1021/acs.nanolett.5c00315. Epub 2025 Apr 12.
Resistance drift due to residual ions limits the accuracy of memristor-based neuromorphic computing. Here, we demonstrate nanofluidic memristors based on voltage-driven ion filling within Ångström channels, immersed in asymmetrically concentrated electrolyte solutions. Inspired by the brain's waste clearance, we restore conductance after 20,000 cycles by removing trapped ions, paving the way for endurance enhancement. The devices exhibit hour-long retention and ultralow energy consumption (∼0.2 fJ per spike per channel). By tuning the voltage, frequency, and pH, we emulate short-term synaptic plasticity. Finally, we demonstrated the first 4 × 4 nanofluidic memristor array capable of recognizing mathematical operators. Our work demonstrated that fluidic memristors are promising for energy-efficient, long-retention, and endurance neuromorphic chips.
由于残留离子导致的电阻漂移限制了基于忆阻器的神经形态计算的准确性。在此，我们展示了基于埃级通道内电压驱动离子填充的纳米流体忆阻器，该忆阻器浸没在不对称浓缩电解质溶液中。受大脑废物清除机制的启发，我们通过去除捕获的离子，在20000次循环后恢复了电导，为提高耐久性铺平了道路。这些器件具有长达一小时的保持时间和超低能耗（每个通道每个尖峰约0.2飞焦）。通过调节电压、频率和pH值，我们模拟了短期突触可塑性。最后，我们展示了首个能够识别数学运算符的4×4纳米流体忆阻器阵列。我们的工作表明，流体忆阻器在制造节能、长保持时间和高耐久性的神经形态芯片方面具有广阔前景。
