Neuromorphic Learning and Recognition in WO3-xThin Film-based Forming-free Flexible Electronic Synapses.

In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO3-x, structured as W/WO3-x/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behaviors fundamental to in-memory computing. We systematically explore synaptic plasticity dynamics by implementing pulse measurements capturing potentiation and depression traits akin to biological synapses under flat and different bending conditions, thereby highlighting its potential suitability for flexible electronic applications. The findings demonstrate that the memristor accurately replicates essential properties of biological synapses, including short-term plasticity (STP), long-term plasticity (LTP), and the intriguing conversion from STP to LTP. Furthermore, other variables, such as paired-pulse facilitation, spike rate-dependent plasticity, spike time-dependent plasticity, pulse duration-dependent plasticity, and pulse amplitude-dependent plasticity, are investigated. Utilizing data from both flat and differently bent synapses, neural network simulations for pattern recognition tasks using the MNIST dataset reveal a high recognition accuracy of ~ 95% with a fast learning speed that requires only 15 epochs to reach saturation.&#xD.