Lithium‐Battery Anode Gains Additional Functionality for Neuromorphic Computing through Metal–Insulator Phase Separation

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.