Materials challenges and opportunities for brain-inspired computing - Zhao, Y. D.; Kang, J. F.; Ielmini, D. (MRS bulletin, 2021)
Inspired by the working principles of the human brain, neuromorphic computing shows great potential in executing cognitive tasks such as learning and adaptation with high energy efficiency. A major challenge is the development of devices and circuits that can naturally replicate the behavior of neurons and synapses, thus reducing the complexity, the energy consumption, and the area of the neuromorphic chip. Recently, much progress has been achieved in realizing hardware neuromorphic circuits with emerging “memristive” materials and devices, which present a wealth of physical phenomena that appear promising for the ad hoc design of virtually any neuromorphic function in the scale of few square nanometers on a silicon chip. In this article, an overview of material opportunities on emerging devices for brain-inspired computing is provided. We will summarize the biological functions of neuromorphic elements, discuss the requirements for their material counterparts and review the recent progress, and illustrate some cognitive computing primitives in hardware networks. Finally, the upcoming materials challenges will be discussed. Graphic abstract