Near-sensor analog computing system based on low-power and self-assembly nanoscaffolded BaTiO3:Nd2O3 memristor

Near-sensor analog computing systems have received a lot of attention as they can effectively reduce the large amount of redundant data transferred between sensor terminals and computing units, thereby shortening the data processing time and reducing power consumption. However, ensuring the reliability and stability of memristor devices used in the hardware circuits of near-sensor analog computing systems remains a considerable challenge. In this paper, we describe a robust ferroelectric memristor based on Pd/BaTiO3:Nd2O3/La0.67Sr0.33MnO3 grown on a silicon structure with SrTiO3 as the buffer layer. Through optimized growth temperature, the device exhibits a low coercive field voltage (-1-2 V), robust endurance characteristics (>10(10) cycles), and a power consumption as low as 0.45 fJ per synaptic event. Also in this study, a near-sensor analog computing system based on an array of pressure sensors and ferroelectric memristors was constructed. It is shown that this system can accurately calculate multiple raw analog pressure signals in real time without the need for peripheral circuitry and that the system can classify object shapes and perform edge detection with a maximum deviation of only about 58.6 nA. This study highlights the great potential of ferroelectric memristors for use as fundamental components of near-sensor analog computing systems.