Low-Power Perovskite-Based Memristors Enable Fused Reservoir Computing and Neuromorphic Vision with Highly Accurate...

Integrating multicolor perception with neuromorphic vision systems, capable of emulating the procedures of image detection, storage, and local processing, represents a significant advancement in artificial visual technologies. However, challenges related to data fusion, system complexity, and stability must be addressed to fully realize the potential of this technology. In this work, a low-dimensional/three-dimensional (LD/3D) halide perovskite heterostructure consisting of Ag/LD perovskitoid/3D CsFAMA/ITO is fabricated, demonstrating excellent stability for 2 months combined with the co-existence of two switching modes, namely volatile and non-volatile. The former mode is leveraged to construct the nodes of the reservoir computing architecture, where the fusion rate of the electrical and optical signals is examined to achieve maximum recognition accuracy of multicolor handwritten MNIST images (84%). An ultra-low power consumption of 400 fJ per synaptic weight change is also recorded during red light irradiation. By combining experiments with different top electrode materials and extensive Density Functional Theory calculations on metal atom diffusion and clustering in the materials of interest, key atomic scale processes are identified that underlie the switching behavior and lead to improved memory performance. The ability of the proposed device configuration to accurately carry out multimodal recognition tasks opens new possibilities for realizing biomimetic systems.