Publication date: 15th April 2025
Energy inefficiency in traditional silicon-based CMOS neuromorphic computing limits the development of advanced artificial intelligence systems. Although CMOS-based in-memory computing reduces the energy required to move data, it suffers from material limitations, including high operating voltages and inability to emulate analogue computation. MXenes are a recently discovered family of 2D transition metal carbides and nitrides
In this study, we fabricated planar Ti₂CTx MXene-based memristors through optimised thin-film deposition. Systematic I-V measurements revealed that the devices exhibit strong retention, high endurance, and low power consumption, outperforming 2D material counterparts. The device properties of MXene-based memristors were then simulated in a neuromorphic crossbar array using the AIHWkit simulator. The memristors were integrated into a spiking neural network with a three-layer architecture of leaky integrate-and-fire neurons for handwritten digit classification on the MNIST dataset. Various characterisation tools, including SEM, XRD, XPS, AFM, and Raman spectroscopy, were employed to analyse the structural, morphological, and chemical properties of the material, providing insights into its impact on device performance.
This work was funded by the School of Engineering and Innovation at The Open University.
We gratefully acknowledge the use of the FIB-SEM Zeiss Crossbeam at the Electron Microscopy Suite, STEM, Open University, and thank the facility staff for their support.
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