@article{Zhou2026, 
author = {Si Yuan Zhou and Wei Sheng Wang and Wen Xiang Tao and Lin Feng Wu and Bo Bo Li and Wan Lin Zhang and Yu Fan Hu and Cong Shan Liu and Li Qiang Zhu},
title = {Acoustic-optical information reservoir system based on triboelectric acoustic sensor tuned oxide photoelectronic neuromorphic transistor},
year = {2026},
journal = {Nano Research},
keywords = {triboelectric acoustic sensor, oxide photoelectronic neuromorphic transistor, acoustic-optical information reservoir system, voice command recognition, drone trajectory recognition},
url = {https://www.sciopen.com/article/10.26599/NR.2026.94908978},
doi = {10.26599/NR.2026.94908978},
abstract = {The rapid developments of internet of things (IoTs) technology have greatly promoted the evolution of human-machine interaction (HMI). Mimicking the auditory and visual mechanisms offers new possibilities for real time multimodal information processing. Here, an acoustic-optical information reservoir system (AOIRS) is proposed by integrating a triboelectric acoustic sensor (TAS) with an indium-zinc-oxide photoelectronic neuromorphic transistor. With an acoustic coupling cavity, the TAS exhibits excellent sound wave collection capacity and durability. A high sensitivity of ~1.1 V/dB is obtained. Thus, interesting neural activities are mimicked on the AOIRS under sound wave signals. The synaptic weight of AOIRS can be repeatedly updated by light and sound stimuli. In addition, AOIRS can act as a physical reservoir to map voice commands and drone trajectory images into high-dimensional conductance states, achieving efficient voice command and drone trajectory recognition via a multi-layer perceptron (MLP) classifier. The average testing accuracy after 200 epochs for voice command and drone trajectory is ~89.8% and 93.6%, respectively. The present AOIRS has great potentials in the fields of voice intelligent sensing and low-altitude industry.}
}