Linear and Symmetric Li-Based Composite Memristors for Efficient Supervised Learning.

Emerging energy-efficient neuromorphic circuits are based on hardware implementation of artificial neural networks (ANNs) that employ the biomimetic functions of memristors. Specifically, crossbar array memristive architectures are able to perform ANN vector-matrix multiplication more efficiently than conventional CMOS hardware. Memristors with specific characteristics, such as ohmic behavior in all resistance states in addition to symmetric and linear long-term potentiation/depression (LTP/LTD), are required in order to fully realize these benefits. Here, we demonstrate a Li-based composite memristor (LCM) that achieves these objectives. The LCM consists of three phases: Li-doped TiO 2 as a Li reservoir, Li 4 Ti 5 O 12 as the insulating phase, and Li 7 Ti 5 O 12 as the metallic phase, where resistive switching correlates with the change in the relative fraction of the metallic and insulating phases. The LCM exhibits a symmetric and gradual resistive switching behavior for both set and reset operations during a full bias sweep cycle. This symmetric and linear weight update is uniquely enabled by the symmetric bidirectional migration of Li ions, which leads to gradual changes in the relative fraction of the metallic phase in the film. The optimized LCM in ANN simulation showed that exceptionally high accuracy in image classification is realized in fewer training steps compared to the nonlinear behavior of conventional memristors.