Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing

Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.

Uniform, fast, and reliable CMOS compatible resistive switching memory

Resistive switching random access memory(RRAM) is considered as one of the potential candidates for next-generation memory. However, obtaining an RRAM device with comprehensively excellent performance, such as high retention and endurance, low variations, as well as CMOS compatibility, etc., is still an open question. In this work, we introduce an insert TaO_(x) layer into HfO_(x)-based RRAM to optimize the device performance. Attributing to robust filament formed in the TaO_(x) layer by a forming operation, the local-field and thermal enhanced effect and interface modulation has been implemented simultaneously. Consequently, the RRAM device features large windows(> 10^(3)), fast switching speed(-10 ns), steady retention(> 72h), high endurance(> 10^(8) cycles), and excellent uniformity of both cycle-to-cycle and device-to-device. These results indicate that inserting the TaO_(x) layer can significantly improve HfO_(x)-based device performance, providing a constructive approach for the practical application of RRAM.

Voltage-control oscillator based on Pt/C/NbO_x/TiN device with highly improved threshold switching performances

Oscillator is a common key component of electronic systems.The periodic signal produced by the oscillator is generally required in various applications,such as the electronic system clock,electronic neurons,and the true random number generator system[1-6].Capacitors and inductors are usually utilized to generate periodic waveforms in the traditional oscillator,which greatly reduces integration and cannot be packaged into chips[2].Oscillators based on a memristor have been proposed as a solution to these issues[7-14].The memristor has attracted great attention and has been widely applied in many fields,such as memory,com?puting,security,etc.