Flexible Low‐Voltage MXene Floating‐Gate Synaptic Transistor for Neuromorphic Computing and Cognitive Learning

Abstract Neuromorphic computing, inspired by the functionality of biological neural networks, has emerged as a promising paradigm for artificial intelligence applications, especially in the field of flexible electronics. Among the various artificial synaptic devices, floating‐gate synaptic transistors exhibit long‐term synaptic plasticity, but they face the challenge of achieving flexible compatibility. In this work, the first demonstration of a flexible MXene floating‐gate synaptic transistor is reported, which uses multiple layers of MXene as floating gates and MXene nanosheets as charge state modulators. The device shows excellent mechanical flexibility and can operate at low voltages, which improves its suitability for wearable electronic devices. It can also emulate Pavlovian conditioned reflexes under external stress, suggesting its potential for cognitive learning. Moreover, the device is utilized for handwritten digit recognition by simulating a fully connected neural network, achieving a high recognition accuracy of 92.0%. This demonstrates its practical applicability in neuromorphic computing. Besides, this research achieves the patterning of MXene and its application in flexible floating‐gate transistors. It provides a new solution for the integrated fabrication of flexible artificial synaptic devices.