Abstract
Memristive devices are the preferred choice for neuromorphic computer architectures, with low-dimensional materials exhibiting unique functionality resembling biological neurons. The ability to adjust these properties presents significant opportunities for artificial neural networks. This review offers a critical investigation of emerging multi-functional (MF) neuromorphic devices enabled by zero-dimensional, one-dimensional, and twodimensional materials, van der Waals heterojunctions, and their mechanisms. It highlights the multiple unique bio-inspired device responses that arises from quantum confinement, interfaces, and low-dimensional topology. The advancements, obstacles, and potential solutions for effective neuromorphic computing using lowdimensional MF neuromorphic systems are surveyed. This overview highlights the appealing attributes of neuromorphic computing for future computations and explores the potential for advancing neuromorphic algorithms based on low-dimensional MF systems. The development of low-dimensional MF neural networks for autonomous system applications is outlined. This review article investigates the integration of physical, physiological, and environmental data through low-dimensional MF neural networks, which is essential for wearable robotic applications. It also provides a prospective analysis of the opportunities and challenges associated with lowdimensional MF neuromorphic materials compared to conventional bulk electronic technologies.