A bioinspired configurable cochlea based on memristors - Cheng, Lingli; Gao, Lili; Zhang, Xumeng; Wu, Zuheng; Zhu, Jiaxue; Yu, Zhaoan; Yang, Yue; Ding, Yanting; Li, Chao; Zhu, Fangduo; Wu, Guangjian; Zhou, Keji; et al. (Frontiers in Neuroscience, 2022)
Cochleas are the basis for biology to process and recognize speech information, emulating which with electronic devices helps us construct high-efficient intelligent voice systems. Memristor provides novel physics for performing neuromorphic engineering beyond complementary metal-oxide-semiconductor technology. This work presents an artificial cochlea based on the shallen-key filter model configured with memristors, in which one filter emulates one channel. We first fabricate a memristor with the TiN/HfOx/TaOx/TiN structure to implement such a cochlea and demonstrate the nonvolatile multilevel states through electrical operations. Then, we build the shallen-key filter circuit and experimentally demonstrate the frequency-selection function of cochlea’s five channels, whose central frequency is determined by the memristor’s resistance. To further demonstrate the feasibility of the cochlea for system applications, we use it to extract the speech signal features and then combine it with a convolutional neural network to recognize the Free Spoken Digit Dataset. The recognition accuracy reaches 92% with 64 channels, compatible with the traditional 64 fourier transform transformation points of mel-frequency cepstral coefficients method with 95% recognition accuracy. This work provides a novel strategy for building cochleas, which has a great potential to conduct configurable, high-parallel, and high-efficient auditory systems for neuromorphic robots.