Organic Resistive Memories for Neuromorphic Electronics

Abstract

With the rapid development of big data, advanced data storage technologies with lower power consumption, faster switching speed, higher integration density and larger storage capacity have become the target of storage electronics in the future. However, traditional Si-based CMOS technology and von Neumann architecture will reach their limits, which cannot satisfy the needs of ultra-high density, ultra-small size, and in-memory computing. Due to their low cost, fast speed, easy handling, high energy efficiency, good scalability and flexibility, organic resistive memories are expected to be candidates for high-density storage, logic computing, and neuromorphic computing. In this chapter, we summarize the research progress of organic resistive switching materials and devices. Firstly, the device structure, storage type and switching mechanism are introduced in detail. Secondly, the design strategies and memory properties of various organic resistive switching materials including organic small molecules, organometallic compounds, polymers, and biomaterials are systematically summarized, while the key performance parameters of the memories are also specifically mentioned. Next, the applications of memristors in neuromorphic computing such as artificial synapses, image recognition, and in-memory arithmetic and logic computing are also discussed. Finally, the current challenges and future directions in developing organic resistive memory materials and their neuromorphic devices are outlined.