Issue

Vol. 17 (2025)

MXene-Ti3C2Tx-Based Neuromorphic Computing: Physical Mechanisms, Performance Enhancement, and Cutting-Edge Computing

https://doi.org/10.1007/s40820-025-01787-0
Kaiyang Wang
Medical Engineering & Engineering Medicine Innovation Center, Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, People’s Republic of China
http://orcid.org/0009-0002-6586-6182
Shuhui Ren
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300071, People’s Republic of China
Yunfang Jia
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300071, People’s Republic of China
http://orcid.org/0000-0002-2093-2118
Xiaobing Yan
Key Laboratory of Brain‑Like Neuromorphic Devices Systems of Hebei Province College of Electron and Information Engineering, Jiaruiyuan Biochip Research Center of Hebei University, Hebei University, Baoding 071002, People’s Republic of China
http://orcid.org/0000-0002-3266-515X
Lizhen Wang
Medical Engineering & Engineering Medicine Innovation Center, Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, People’s Republic of China
http://orcid.org/0000-0002-9658-659X
Yubo Fan
Medical Engineering & Engineering Medicine Innovation Center, Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, People’s Republic of China
http://orcid.org/0000-0002-3480-4395

Corresponding Author: Yubo Fan

yubofan@buaa.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 273

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.


Highlights:
1 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.
2 The review outlines three key engineering strategies: doping engineering, interfacial engineering, and structural engineering, while also providing comprehensive guidance for material and device improvement.
3 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.

Keywords

Neuromorphic device MXene-Ti3C2Tx Physical mechanisms Performance improvement Cutting-edge computing
Wang, Kaiyang, Shuhui Ren, Yunfang Jia, Xiaobing Yan, Lizhen Wang, and Yubo Fan. 2025. “MXene-Ti3C2Tx-Based Neuromorphic Computing: Physical Mechanisms, Performance Enhancement, and Cutting-Edge Computing”. Nano-Micro Letters 17 (May):273. https://doi.org/10.1007/s40820-025-01787-0.
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