Structural Transformation of 2D InSe Toward Ultrafast and Energy‐Efficient Non‐Volatile Memristive Switching

Abstract 2D materials provide a versatile platform for developing memristor‐based in‐memory computing systems, with potential to address some limitations of conventional von Neumann architectures. However, meeting the stringent requirements for precision, stability, and energy efficiency in neural network hardware remains a challenge due to the intrinsic properties of many 2D materials. In this work, a controllable ultraviolet ozone (UVO) treatment is introduced to engineer the properties of 2D indium selenide (InSe) by introducing a tailored combination of defects, amorphous regions, and oxidized phases. This modification improves the structural stability and vertical conductivity of InSe, and promotes ion migration, enabling a transition from non‐switching to stable nonvolatile resistive switching (RS) behavior. The resulting memristors exhibit uniform RS characteristics, with low variability in switching voltage (5.8%) and a tunable on/off ratio ranging from 10 2 to 10 5 . In addition, the devices demonstrate sub‐20 ns switching speeds and can emulate artificial neural networks (ANNs) with recognition accuracy comparable to software‐based implementations. Hardware‐based convolutional image processing with improved power efficiency is further demonstrated, underscoring the potential of UVO‐InSe memristors for energy‐efficient neuromorphic computing applications.