• 文献标题:   Nonconventional Analog Comparators Based on Graphene and Ferroelectric Hafnium Zirconium Oxide
  • 文献类型:   Article
  • 作  者:   LIU JL, RYU H, ZHU WJ
  • 作者关键词:   ferroelectric hafnium oxide, graphene, image classifier, inmemory analog computing, motion detection
  • 出版物名称:   IEEE TRANSACTIONS ON ELECTRON DEVICES
  • ISSN:   0018-9383 EI 1557-9646
  • 通讯作者地址:  
  • 被引频次:   1
  • DOI:   10.1109/TED.2021.3049757
  • 出版年:   2021

▎ 摘  要

Unlike transitional semiconductors, graphene has zero bandgap and symmetric electron/hole transport, which leads to unique V-shaped transfer characteristics. Using this property, we design and demonstrate a new type of comparator, which can calculate the absolute distance between two signals, vertical bar A - B vertical bar, directly. Dual-gate graphene transistors with ferroelectric hafnium zirconium oxide are fabricated to serve as the basic units of the comparators. We show that the remanent polarization of the ferroelectric hafnium oxide can reach similar to 30 mu C/cm(2 )and the output current of the comparator can serve as a scalar indicator of the similarity level between two signals. The embedded ferroelectric layer can store the reference signal in situ, which will reduce the energy consumption and latency related to the data transport. Furthermore, we demonstrate the feasibility of using ferroelectric graphene comparator in image classification and motion detection. Using the k-nearest neighbors (KNNs) algorithm, we show that the graphene comparator arrays can recognize the handwritten digits in the modified national institute of standards and technology (MNIST) data set with over 80% accuracy. These ferroelectric graphene comparators will have broad applications in robotics, security system, self-driving vehicles, and sensor networks.