Jaewook Yoo, Hyeonjun Song, Hong-Yu Lee, Seongbin Lim, Soyeon Kim, K. Heo, H. Bae
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引用次数: 0
Abstract
The AI and IoT era requires software and hardware capable of efficiently processing massive amounts data quickly and at a low cost. However, there are bottlenecks in existing Von Neumann structures, including the difference in the operating speed of current-generation DRAM and Flash memory systems, the large voltage required to erase the charge of nonvolatile memory cells, and the limitations of scaled-down systems. Ferroelectric materials are one exciting means of breaking away from this structure, as Hf-based ferroelectric materials have a low operating voltage, excellent data retention qualities, and show fast switching speed, and can be used as non-volatile memory (NVM) if polarization characteristics are utilized. Moreover, adjusting their conductance enables diverse computing architectures, such as neuromorphic computing with analog characteristics or ‘logic-in-memory’ computing with digital characteristics, through high integration. Several types of ferroelectric memories, including two-terminal-based FTJs, three-terminal-based FeFETs using electric field effect, and FeRAMs using ferroelectric materials as capacitors, are currently being studied. In this review paper, we include these devices, as well as a Fe-diode with high on/off ratio properties, which has a similar structure to the FTJs but operate with the Schottky barrier modulation. After reviewing the operating principles and features of each structure, we conclude with a summary of recent applications that have incorporated them.
ElectronicsComputer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍:
Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.