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The adjustable conductance of a two-terminal memristor in a crossbar array can facilitate vector-matrix multiplication in one step, making the memristor a promising synapse for efficiently implementing neuromorphic computing. To achieve controllable and gradual switching of multi-level conductance, important for neuromorphic computing, a theoretical design of a superlattice-like (SLL) structure switching layer for the multi-level memristor is proposed and validated, refining the growth of conductive filaments (CFs) and preventing CFs from the abrupt formation and rupture. Ti/(HfO x /AlO y ) SLL /TiN memristors are shown with transmission electron microscopy , X-ray photoelectron spectroscopy , and ab initio calculation findings corroborate the SLL structure of HfO x /AlO y film. The optimized SLL memristor achieves outstanding conductance modulation performance with linearly synaptic weight update (nonlinear factor 𝜶 = 1.06), and the convolutional neural network based on the SLL memristive synapse improves the handwritten digit recognition accuracy to 94.95%. Meanwhile, this improved synaptic device has a fast operating speed (30 ns), a long data retention time (≥ 10 4 s at 85 °C), scalability, and CMOS process compatibility. Finally, its physical nature is explored and the CF evolution process is characterized using nudged elastic band calculations and the conduction mechanism fitting. In this work, as an example the HfO x /AlO y SLL memristor provides a design viewpoint and optimization strategy for neuromorphic computing.

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