Memristive Artificial Synapses Based on Brownmillerite for Endurable Weight Modulation.

Exploring a computing paradigm that blends memory and computation functions is essential for artificial synapses. While memristors for artificial synapses are widely studied due to their energy-efficient structures, random filament conduction in general memristors makes them less preferred for endurability in long-term synaptic modulation. Herein, the topotactic phase transition (TPT) in brownmillerite-phased (110)-SrCoO (SCO) is harnessed to enhance the reversibility of oxygen ion migration through 1-D oxygen vacancy channels. By employing a heteroepitaxial structured 2-terminal configuration of Au/SCO/SrRuO/SrTiO, the brownmillerite SCO-based synapse artificial synapses are exploited. Demonstration of the TPT behavior is corroborated by comparing oxygen migration energy by density-functional theory calculations and experimental results, and by monitoring the voltage pulse-induced peak shift in the Raman spectra of SCO. With the voltage pulse-driven TPT behaviors, it is reliably characterized by linear, symmetric, and endurable long-term potentiation and depression performances. Notably, the durability of the TPT-based weight control mechanism is demonstrated by achieving consistent and noise-free weight updates over 32 000 iterations across 640 cycles. Furthermore, learning performances based on deep neural networks and convolutional neural networks on various image datasets yielded very high recognition accuracy. The work offers valuable insights into designing memristive synapses that enable reliable weight updates in neural networks.