Key Takeaways & Executive Findings
- •• This review reveals the advantages of MXene-Ti3C2Tx for neuromorphic devices, classifies the core physical mechanisms, and outlines strategies to drive targeted optimization and future innovation. • The review outlines three key engineering strategies: doping engineering, interfacial engineering, and structural engineering, while also providing comprehensive guidance for material and device improvement. • MXene-Ti3C2Tx-based devices demonstrate groundbreaking potential in next-generation computing, such as near-sensor computing and in-sensor computing, enabling faster and more energy-efficient data processing directly at the sensor level. • The review compiles a comprehensive table of research results and discusses challenges, prospects, and feasibility for practical applications, laying a solid theoretical foundation for further exploration.
Abstract
Neuromorphic devices have shown great potential in simulating the function of biological neurons due to their efficient parallel information processing and low energy consumption. MXene-Ti3C2Tx, an emerging two-dimensional material, stands out as an ideal candidate for fabricating neuromorphic devices. Its exceptional electrical performance and robust mechanical properties make it an ideal choice for this purpose. This review aims to uncover the advantages and properties of MXene-Ti3C2Tx in neuromorphic devices and to promote its further development. Firstly, we categorize several core physical mechanisms present in MXene-Ti3C2Tx neuromorphic devices and summarize in detail the reasons for their formation. Then, this work systematically summarizes and classifies advanced techniques for the three main optimization pathways of MXene-Ti3C2Tx, such as doping engineering, interface engineering, and structural engineering. Significantly, this work highlights innovative applications of MXene-Ti3C2Tx neuromorphic devices in cutting-edge computing paradigms, particularly near-sensor computing and in-sensor computing. Finally, this review carefully compiles a table that integrates almost all research results involving MXene-Ti3C2Tx neuromorphic devices and discusses the challenges, development prospects, and feasibility of MXene-Ti3C2Tx-based neuromorphic devices in practical applications, aiming to lay a solid theoretical foundation and provide technical support for further exploration and application of MXene-Ti3C2Tx in the field of neuromorphic devices.
1. Introduction
Artificial intelligence technologies have further enhanced the global computing infrastructure, with breakthroughs in high-performance computing and intelligent computing greatly improving the ability of computers to handle large-scale data and complex computing tasks [1–3]. However, the rapid development of artificial intelligence technology has increasingly demanded data intensification and resource miniaturization. The traditional separation architecture of perception, storage, and computing is gradually unable to meet the requirements of rapid processing of massive data and low-energy information transmission, becoming a bottleneck problem in the development of the artificial intelligence field [4, 5].
The neuromorphic system that integrates sensing, storage, and computing functions can perceive external signals while storing and computing in real-time, quickly, and efficiently simulating brain thinking, bringing new development opportunities to the field of artificial intelligence. This computing method often relies on emerging neuromorphic devices, which are electronic components that can mimic the function of biological neurons and synapses [6, 7]. For example, memristors can modulate their resistance value based on the accumulated charge or passed voltage, and synaptic transistors can modulate the channel’s conductivity by controlling the gate voltage [8, 9]. Traditional memristors and synaptic transistors have encountered challenges in terms of size reduction, energy consumption, and stability. Therefore, many researchers are dedicated to finding new electronic materials to enhance and improve the performance of neuromorphic devices.
Two-dimensional (2D) nanomaterials are single-layer or few-layer novel nanomaterials, which can be prepared by a variety of methods, such as chemical vapor deposition, mechanical exfoliation, and solution exfoliation [10, 11]. So far, many types of 2D materials have been explored for neuromorphic applications, offering unique properties that could overcome the limitations of traditional materials.
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Kaiyang Wang, Shuhui Ren, Yunfang Jia, Xiaobing Yan, Lizhen Wang, Yubo Fan (2025). MXene-Ti3C2Tx-Based Neuromorphic Computing: Physical Mechanisms, Performance Enhancement, and Cutting-Edge Computing. Nano-Micro Letters. https://doi.org/10.1007/s40820-025-01787-0
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Frequently Asked Questions
What are the key advantages of MXene-Ti3C2Tx for neuromorphic devices?
MXene-Ti3C2Tx offers exceptional electrical performance and robust mechanical properties, making it an ideal candidate for neuromorphic devices. Its two-dimensional structure enables efficient parallel information processing and low energy consumption, which are crucial for simulating biological neurons.
What are the main physical mechanisms in MXene-Ti3C2Tx neuromorphic devices?
The review categorizes several core physical mechanisms, including ion migration, charge trapping, and phase transitions, which are responsible for the synaptic and neuronal behaviors in MXene-Ti3C2Tx devices. These mechanisms are detailed to understand the device operation.
What engineering strategies are proposed to enhance MXene-Ti3C2Tx neuromorphic devices?
The review outlines three key engineering strategies: doping engineering, interfacial engineering, and structural engineering. These approaches aim to optimize material properties and device performance, providing comprehensive guidance for improvement.
How do MXene-Ti3C2Tx devices contribute to cutting-edge computing paradigms?
MXene-Ti3C2Tx-based devices show groundbreaking potential in near-sensor and in-sensor computing, enabling faster and more energy-efficient data processing directly at the sensor level. This integration of sensing and computing reduces latency and power consumption.
What are the future prospects and challenges for MXene-Ti3C2Tx neuromorphic devices?
The review discusses challenges such as scalability, stability, and integration with existing technologies. However, it highlights the feasibility and development prospects, aiming to lay a solid theoretical foundation for practical applications in neuromorphic computing.
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