Organic Nanomaterials in Memristor-Based Devices for Information Processing and Artificial Intelligence Applications: A Review

In the era of information explosion and artificial intelligence, as Moore’s Law gradually approaches its limits, the traditional Von Neumann architecture can no longer meet the growing demands for massive storage capacity and efficient computation. In recent years, organic memristors based on nanostructured organic materials have emerged as a research hotspot for next-generation electronic devices due to their unique advantages in information storage and intelligent computing. As computationally intensive applications continue to expand, these nanoscale devices offer promising solutions to overcome the energy efficiency and scalability limitations of traditional architectures. Compared to conventional storage technologies, organic memristors offer advantages such as compact size, low power consumption, nonvolatility, and nanoscale tunability, making them attractive for applications in flexible electronics, low-energy storage systems, and integrated circuit design. Not only that, but also it has a wide range of applications in emerging areas such as sensors, multilevel storage, synapse simulation, neuromorphic computing, and other applications. In particular, the nanoscale features of organic materials, such as size-dependent properties, play a crucial role in optimizing the performance of organic memristors. In this review, we first review the feasibility of nanostructured organic materials in memristor applications and the research progress of organic memristors, focusing on the characteristics of organic materials in multifunctional applications (transmission, storage, doping), and introduce the related applications of organic memristors in information storage and artificial intelligence. We also summarized the switching mechanism of organic-nanomaterial-based memristors and explored how nanoscale effects affect the switching dynamics, storage stability, and overall performance of these devices. Finally, strategies to improve the memory performance of organic memristors are summarized, the challenges to be faced in information storage and artificial intelligence applications are discussed, and an outlook for future development is given.