Tunneling‐Controlled Fusion of Short‐ and Long‐Term Memory in SiO ₂ /HfO ₂ ‐Based Neuromorphic Device for Time‐Series Prediction

Prediction is a needful action before decision-making, typically related to the interaction of historical information and current states. It requires computing with information on multiple timescales, meaning intrinsic dynamics in short-long-term plasticity. However, current implementations of short-long-term memory are still limited by unstable retention in LTM mode and high energy consumption. Here a trap-assisted tunneling strategy is used in Schottky-based optical synapse, achieving the tunneling-switched short-long-term memory transformation. Based on the devices, the spike-timing-dependent plasticity and spike-number-dependent plasticity have been programmed with a low switching energy of 1 pJ that is close to the human brain. By adjustment of light-pulse inputs, the adaptive short-to-long-term transition can be achieved with a clear mode jump. Furthermore, the formed LTM both in stability (only 0.2 % loss ratio over a span of 50 s) and energy requirement (as low as 3.05 × 10^-11 W) shows desired improvement compared to other devices implementing short-long-term memory. By further mapping device parameters to a long short-term memory network, it allows for the analysis time-series data and then to make precise long-term predictions with an error ratio of as low as 4.465 %. This research can offer a hopeful solution to address the precise trend prediction, holding substantial promise for intelligent applications.