Cheng Lingli, Gao Lili, Zhang Xumeng, Wu Zuheng, Zhu Jiaxue, Yu Zhaoan, Yang Yue, Ding Yanting, Li Chao, Zhu Fangduo, Wu Guangjian, Zhou Keji, Wang Ming, Shi Tuo, Liu Qi
Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China.
Frontier Institute of Chip and System, Fudan University, Shanghai, China.
Front Neurosci. 2022 Oct 3;16:982850. doi: 10.3389/fnins.2022.982850. eCollection 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/HfO/TaO/TiN structure to implement such a cochlea and demonstrate the non-volatile 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 . 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.
耳蜗是生物学处理和识别语音信息的基础，通过电子设备模拟耳蜗有助于我们构建高效的智能语音系统。忆阻器为超越互补金属氧化物半导体技术的神经形态工程提供了新颖的物理原理。这项工作提出了一种基于由忆阻器配置的浅键滤波器模型的人工耳蜗，其中一个滤波器模拟一个通道。我们首先制造了具有TiN/HfO/TaO/TiN结构的忆阻器来实现这样的耳蜗，并通过电操作展示了非易失性多电平状态。然后，我们构建了浅键滤波器电路，并通过实验证明了耳蜗五个通道的频率选择功能，其中心频率由忆阻器的电阻决定。为了进一步证明耳蜗在系统应用中的可行性，我们用它来提取语音信号特征，然后将其与卷积神经网络相结合来识别……。64个通道时识别准确率达到92%，与传统的具有95%识别准确率的梅尔频率倒谱系数方法的64个傅里叶变换点兼容。这项工作为构建耳蜗提供了一种新颖的策略，具有为神经形态机器人构建可配置、高并行和高效听觉系统的巨大潜力。
