Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing

Abstract

Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing. In this paper, an oscillation neuron based on a low-variability Ag nanodots (NDs) threshold switching (TS) device with low operation voltage, large on/off ratio and high uniformity is presented. Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V. The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance. It can then be used to evaluate the resistive random-access memory (RRAM) synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing. Meanwhile, simulation results show that a large RRAM crossbar array (> 128 × 128) can be supported by our oscillation neuron owing to the high on/off ratio (> 108) of Ag NDs TS device. Moreover, the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy (< 1%). Therefore, the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.