Mai V H, Moradpour A, Senzier P Auban, Pasquier C, Wang K, Rozenberg M J, Giapintzakis J, Mihailescu C N, Orfanidou C M, Svoukis E, Breza A, Lioutas Ch B, Franger S, Revcolevschi A, Maroutian T, Lecoeur P, Aubert P, Agnus G, Salot R, Albouy P A, Weil R, Alamarguy D, March K, Jomard F, Chrétien P, Schneegans O
1] Laboratoire de Génie Électrique de Paris, CNRS-UMR 8507, Universités UPMC et Paris-Sud, Supélec, F-91192 Gif-sur-Yvette, France [2] Institut d'Électronique Fondamentale, CNRS-UMR 8622, Université Paris-Sud, 91405 Orsay, France.
Laboratoire de Physique des Solides, CNRS-UMR 8502, Université Paris-Sud, F-91405 Orsay, France.
Sci Rep. 2015 Jan 14;5:7761. doi: 10.1038/srep07761.
The phenomenon of resistive switching (RS), which was initially linked to non-volatile resistive memory applications, has recently also been associated with the concept of memristors, whose adjustable multilevel resistance characteristics open up unforeseen perspectives in cognitive computing. Herein, we demonstrate that the resistance states of Li(x)CoO2 thin film-based metal-insulator-metal (MIM) solid-state cells can be tuned by sequential programming voltage pulses, and that these resistance states are dramatically dependent on the pulses input rate, hence emulating biological synapse plasticity. In addition, we identify the underlying electrochemical processes of RS in our MIM cells, which also reveal a nanobattery-like behavior, leading to the generation of electrical signals that bring an unprecedented new dimension to the connection between memristors and neuromorphic systems. Therefore, these LixCoO2-based MIM devices allow for a combination of possibilities, offering new perspectives of usage in nanoelectronics and bio-inspired neuromorphic circuits.
电阻开关（RS）现象最初与非易失性电阻存储器应用相关联，近来也与忆阻器的概念联系在一起，忆阻器的可调多级电阻特性为认知计算开辟了意想不到的前景。在此，我们证明基于Li(x)CoO2薄膜的金属-绝缘体-金属（MIM）固态电池的电阻状态可通过顺序编程电压脉冲进行调节，并且这些电阻状态极大地依赖于脉冲输入速率，从而模拟生物突触可塑性。此外，我们确定了我们的MIM电池中电阻开关的潜在电化学过程，这也揭示了类似纳米电池的行为，导致产生电信号，为忆阻器与神经形态系统之间的联系带来了前所未有的新维度。因此，这些基于LixCoO2的MIM器件具有多种可能性的组合，为纳米电子学和受生物启发的神经形态电路提供了新的应用前景。
