Preparation of MXene-based hybrids and their application in neuromorphic devices

Abstract

Abstract The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption, making it difficult to meet the computing needs of artificial intelligence (AI). Neuromorphic computing systems, with massively parallel computing capability and low power consumption, have been considered as an ideal option for data storage and AI computing in the future. Memristor, as the fourth basic electronic component besides resistance, capacitance and inductance, is one of the most competitive candidates for neuromorphic computing systems benefiting from the simple structure, continuously adjustable conductivity state, ultra-low power consumption, high switching speed and compatibility with existing CMOS technology. The memristors with applying MXene-based hybrids have attracted significant attention in recent years. Here, we introduce the latest progress in the synthesis of MXene-based hybrids and summarize their potential applications in memristor devices and neuromorphological intelligence. We explore the development trend of memristors constructed by combining MXenes with other functional materials and emphatically discuss the potential mechanism of MXenes-based memristor devices. Finally, the future prospects and directions of MXene-based memristors are briefly described.