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Low-dimensional optoelectronic memristors: From quantum confinement to neuromorphic vision
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DOI：10.1016/j.mser.2025.101115.png)
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Abstract
En 中文
The von Neumann architecture's inherent separation of memory and computation has become a critical bottleneck in the era of big data, driving the search for integrated computing-memory solutions. Memristors, with their intrinsic ability to unify storage and processing, have emerged as a transformative platform. The exceptional physical properties of low-dimensional materials have played a critical role in this progress, enabling unprecedented device miniaturization, increased storage density, and tunable optoelectronic functionality through their outstanding electronic, optical, and quantum characteristics. This review explores the pivotal role of low-dimensional materials in revolutionizing optoelectronic memristors, focusing on their quantum confinement effects, tunable optoelectronic properties, and neuromorphic applications. We systematically analyze how 0D quantum dots enable light-modulated conductive pathways through precise carrier trapping, 1D nanowires leverage anisotropic charge transport for ultrafast photoresponse, and 2D materials facilitate heterostructure engineering to enhance switching stability. We then deeply analyze the transformative impact of optoelectronic memristors based on low-dimensional materials in neuromorphic computing, particularly their remarkable advantages in simulating complex synaptic dynamics and developing low-energy artificial vision systems. Finally, we specifically outline future research directions, focusing on overcoming bottlenecks in the precise synthesis and scalable fabrication of low-dimensional materials, and leveraging their exceptional optoelectronic properties and tunable quantum characteristics to emulate more intricate synaptic dynamics, thereby bridging the gap between electronic and biological systems. These efforts aim to amplify the role of optoelectronic memristors in future neuromorphic computing and highly integrated chip applications.
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