ScholarWorks@동국대학교

초록

Neuromorphic computing systems inspired by the human brain emulate biological synapses electronically for information handling and processing. Recently, memristive switching devices so-called ‘memristors’ are emerging as an essential constituent of artificial intelligence (AI) and internet-of-thing (IoT) circuits toward the development of energy-efficient intelligent systems proficient with neuromorphic computing features to huddle up the current limits of the conventional von Neumann computing system. Memristors have gained attention to realizing artificial synapses by altering resistance analogous to biological counterparts. ZnO-based memristors allow the formation of two-terminal crossbar architectures with metal/insulator/metal (MIM) cells (i.e., top electrode/active layer/bottom electrode), and the device's interactivity can be drastically increased. The availability of multiple resistance states in ZnO-based memristors can lead to high-density data storage capacity and artificial synapse. In this review, we discussed the state-of-art of n-type ZnO-polymer (n-ZnO:Poly) hybrid nanocomposite-based memristors, focusing on their intrinsic mechanisms of resistive switching, progress, advancement, and the challenges to the development of high-performance memristive devices. Additionally, the synaptic functions of n-ZnO:Poly nanocomposite-based memristors are explored as artificial synapses for neural networks to emulate synaptic plasticity. Finally, the key requirements for AI and IoT electronics are highlighted in the future perspectives and opportunities for the development of low-power and high-density memristors as artificial synapses with synaptic weight tunability and reliable synaptic plasticity. © 2023 Elsevier Ltd

키워드