The identification of ferroelectricity in oxides such as hafnium oxide,which are compatible with the contemporary semiconductor fabrication techniques,has contributed to a resurgence of ferroelectric devices in cuttin...The identification of ferroelectricity in oxides such as hafnium oxide,which are compatible with the contemporary semiconductor fabrication techniques,has contributed to a resurgence of ferroelectric devices in cutting-edge microelectronics.In a transistor structure,ferroelectric devices play the role of connecting a ferroelectric material to a semiconductor,which combines memory and logic operations at the level of a single device,thus meeting some of the most essential hardware requirements for new paradigms for artificial intelligence(A.I)chips.In this review,we addressed the issues associated with high-volume fabrication at advanced technology nodes(≤10 nm) at the material and device level.Moreover,we also reviewed the advancement of A.I chips such as neuro-inspired computer chips.For neuro-inspired A.I chips based on nonvolatile memory,four important metrics are suggested for benchmarking:computing density,energy efficiency,learning capability,and computing accuracy.It is inferred that ferroelectric devices can be a major hardware element in the design of future A.I chips,which will leads to an innovative approach to electronics that is termed ferroelectronics.展开更多
基金funded by the National Key Research and Development Program,grant umber 2022YFE0124200the National Natural Science Foundation of China,grant number:U2241221.
文摘The identification of ferroelectricity in oxides such as hafnium oxide,which are compatible with the contemporary semiconductor fabrication techniques,has contributed to a resurgence of ferroelectric devices in cutting-edge microelectronics.In a transistor structure,ferroelectric devices play the role of connecting a ferroelectric material to a semiconductor,which combines memory and logic operations at the level of a single device,thus meeting some of the most essential hardware requirements for new paradigms for artificial intelligence(A.I)chips.In this review,we addressed the issues associated with high-volume fabrication at advanced technology nodes(≤10 nm) at the material and device level.Moreover,we also reviewed the advancement of A.I chips such as neuro-inspired computer chips.For neuro-inspired A.I chips based on nonvolatile memory,four important metrics are suggested for benchmarking:computing density,energy efficiency,learning capability,and computing accuracy.It is inferred that ferroelectric devices can be a major hardware element in the design of future A.I chips,which will leads to an innovative approach to electronics that is termed ferroelectronics.
