Dual-Wavelength Synaptic Simulator ReS₂/Ta₂NiSe₅ for Multi-Timescale Learning in Neuromorphic Computing

To address the limitations of silicon-based devices in neuromorphic computing, this study proposes a dual-wavelength photomodulated synaptic device based on the ReS₂/Ta₂NiSe₅ heterojunction. Through heterojunction band engineering, wavelength-selective synaptic plasticity is achieved, leveraging the photothermal effect at 1550 nm wavelength and the photoelectric effect at 520 nm wavelength. This enables a wavelength-selective synaptic weight update strategy, addressing the limitation of conventional single-wavelength devices that lack multi-timescale adaptability. The device exhibits distinct relaxation timescales, with slow synaptic weight updates (≈17 s) at 1550 nm wavelength due to photothermal-driven charge trapping, and (≈6 s) at 520 nm wavelength enabled by efficient photoelectric carrier dynamics. This dual-timescale synaptic plasticity enables precise control over synaptic weight adaptation, supporting multi-timescale learning. Validation through LeNet convolutional neural network (CNN) training on the Extended MNIST (EMNIST) dataset confirms that the dual-wavelength cooperative learning strategy significantly improves both training efficiency and generalization, achieving a final accuracy of 95.5%. These findings highlight the potential of dual-wavelength heterojunction photonic synaptic devices for adaptive neuromorphic computing, offering a scalable approach for multi-timescale learning in future photonic artificial intelligence (AI) architectures.