Control-Etched Ti₃C₂T MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic...

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti₃C₂T MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti₃C₂T) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti₃C₂T)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼10³ ON/OFF ratio, high endurance of ∼10⁴ cycles, multilevel resistance states, and long data retention measured up to ∼10⁶ s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti₃C₂T MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for ∼10⁴ cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.