Neuromorphic Artificial Vision Systems Based on Reconfigurable Ion-modulated Memtransistors    

Conventional vision systems suffer from lots of data handling between memory and processing units. Inspired by how humans recognize noisy images and the flexible modulation on the timescale of ion dynamics inside an emerging memtransistor, we report a novel neuromorphic vision system based on the ion-modulated memtransistors. By controlling the ion doping processes under adequate stimuli strengths, both short-term and long-term ion dynamics can be utilized to deliver energy-efficient data processing. When dealing with image reconstructions, the short-term accumulation effect of the device can help filter noises in a set of received noisy images while enhancing the original pattern information. The increased contrast can help distinguish the actual contents. To demonstrate systematic performances with the reconfiguration of devices, we extract the nonlinear relationship between channel conductance variation and the amplitude of gate pulses into the network-level simulation. Also, with the nonvolatile conductance change characteristic, the task of recognizing noisy images is performed to verify the versatility of ion-modulated memtransistors in the neuromorphic artificial vision systems.