Specialized hardware for neural networks requires materials with tunable symmetry, retention, and speed at low power consumption. The study proposes lithium titanates, originally developed as Li-ion battery anode materials, as promising candidates for memristive-based neuromorphic computing hardware. By using ex- and in operando spectroscopy to monitor the lithium filling and emptying of structural positions during electrochemical measurements, the study also investigates the controlled formation of a metallic phase (Li7 Ti5 O12 ) percolating through an insulating medium (Li4 Ti5 O12 ) with no volume changes under voltage bias, thereby controlling the spatially averaged conductivity of the film device. A theoretical model to explain the observed hysteretic switching behavior based on electrochemical nonequilibrium thermodynamics is presented, in which the metal-insulator transition results from electrically driven phase separation of Li4 Ti5 O12 and Li7 Ti5 O12 . Ability of highly lithiated phase of Li7 Ti5 O12 for Deep Neural Network applications is reported, given the large retentions and symmetry, and opportunity for the low lithiated phase of Li4 Ti5 O12 toward Spiking Neural Network applications, due to the shorter retention and large resistance changes. The findings pave the way for lithium oxides to enable thin-film memristive devices with adjustable symmetry and retention.

中文翻译：

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锂电池阳极通过金属-绝缘子相分离获得了用于神经形态计算的附加功能。

用于神经网络的专用硬件需要具有低功耗的对称性，保持力和速度可调的材料。这项研究提出，钛酸锂最初是作为锂离子电池阳极材料开发的，它有望成为基于忆阻性神经形态计算硬件的候选材料。通过使用操作前和操作中的光谱监测电化学测量期间锂的填充和结构位置的空化，该研究还研究了通过绝缘介质（Li4 Ti5 O12）渗出的金属相（Li7 Ti5 O12）的受控形成。体积在电压偏置下发生变化，从而控制薄膜器件的空间平均电导率。提出了基于电化学非平衡热力学解释观察到的磁滞开关行为的理论模型，其中金属-绝缘体转变是由Li4 Ti5 O12和Li7 Ti5 O12的电驱动相分离产生的。据报道，由于保留时间短且电阻变化大，Li7 Ti5 O12的高度锂化阶段具有深的神经网络应用的能力，这是因为它具有较大的保留率和对称性，而Li4 Ti5 O12的低锂化阶段也有向掺入神经网络应用的机会。 。这些发现为锂氧化物铺平了可调节对称性和保持力的薄膜忆阻器件铺平了道路。由于保留时间较短且电阻变化较大，因此有可能使Li4 Ti5 O12的低锂化阶段应用于尖刺神经网络应用。这些发现为锂氧化物铺平了可调节对称性和保持力的薄膜忆阻器件铺平了道路。由于保留时间较短且电阻变化较大，因此有可能使Li4 Ti5 O12的低锂化阶段应用于尖刺神经网络应用。这些发现为锂氧化物铺平了可调节对称性和保持力的薄膜忆阻器件铺平了道路。