Wei Yanan, Liu Youxing, Lin Qijie, Liu Tianhua, Wang Song, Chen Hao, Li Congqi, Gu Xiaobin, Zhang Xin, Huang Hui
College of Materials Science and Opto-Electronic Technology and Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China.
School of Materials Science and Engineering, Peking University, Beijing, 100871, People's Republic of China.
Nanomicro Lett. 2023 May 24;15(1):133. doi: 10.1007/s40820-023-01116-3.
The neuromorphic systems for sound perception is under highly demanding for the future bioinspired electronics and humanoid robots. However, the sound perception based on volume, tone and timbre remains unknown. Herein, organic optoelectronic synapses (OOSs) are constructed for unprecedented sound recognition. The volume, tone and timbre of sound can be regulated appropriately by the input signal of voltages, frequencies and light intensities of OOSs, according to the amplitude, frequency, and waveform of the sound. The quantitative relation between recognition factor (ζ) and postsynaptic current (I = I - I) is established to achieve sound perception. Interestingly, the bell sound for University of Chinese Academy of Sciences is recognized with an accuracy of 99.8%. The mechanism studies reveal that the impedance of the interfacial layers play a critical role in the synaptic performances. This contribution presents unprecedented artificial synapses for sound perception at hardware levels.
用于声音感知的神经形态系统对未来的仿生电子学和类人机器人有着极高的要求。然而，基于音量、音调及音色的声音感知仍不为人所知。在此，构建了有机光电突触（OOS）用于前所未有的声音识别。根据声音的幅度、频率和波形，声音的音量、音调及音色可通过OOS的电压、频率和光强输入信号进行适当调节。建立了识别因子（ζ）与突触后电流（I = I - I）之间的定量关系以实现声音感知。有趣的是，对中国科学院大学的铃声识别准确率达到了99.8%。机理研究表明，界面层的阻抗在突触性能中起关键作用。这一成果在硬件层面展示了用于声音感知的前所未有的人工突触。
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