Ultra-Low-Power and Reconfigurable Optoelectronic Memtransistor Based on Vertical Nb-WSe₂/Te van der Waals...

The development of high-performance neuromorphic computing hardware is a key pathway to overcome the Von Neumann architecture's energy efficiency bottleneck. Among these hardware solutions, reconfigurable and low-power synaptic memtransistors are regarded as promising candidates for building high-energy-efficiency brain-like systems. Here, we fabricated an ultra-low-power, reconfigurable optoelectronic memtransistor using a vertical Nb-doped WSe₂ (Nb-WSe₂)/Te van der Waals (vdW) heterostructure. Under optical stimulation, the device can reproducibly emulate paired-pulse facilitation, short-term plasticity, and long-term plasticity, offering programmable AND/OR logic via combined photoelectronic control. Critically, the single-pulse energy consumption can decrease below 1 aJ, demonstrating excellent energy efficiency for optoelectronic synapses (about 4 orders of magnitude lower than that of the biological synapse). Utilizing wavelength-selective light pulses, we further emulate Pavlovian associative learning, highlighting the device's capability for multimodal synaptic conditioning. Finally, when used as the building block of a convolutional neural network (CNN), the memtransistor array achieves 92.32% accuracy on the CIFAR-10 benchmark. It retains 72.75% accuracy under multilevel noise, demonstrating strong classification performance and robustness. These results indicate that Nb-WSe₂/Te vdW memtransistors are a promising candidate for constructing ultra-low-power, reconfigurable neuromorphic and brain-inspired computing hardware.