TY - JOUR
T1 - Nonconventional Analog Comparators Based on Graphene and Ferroelectric Hafnium Zirconium Oxide
AU - Liu, Jialun
AU - Ryu, Hojoon
AU - Zhu, Wenjuan
N1 - Funding Information:
Manuscript received December 5, 2020; accepted January 3, 2021. Date of publication January 22, 2021; date of current version February 24, 2021. This work was supported in part by the National Science Foundation (NSF) under Grant ECCS 16-53241 CAR and in part by the Office of Naval Research (ONR) under Grant NAVY N00014-17-1-2973. The work of Hojoon Ryu was supported by the Kwanjeong Educational Foundation. The review of this article was arranged by Editor B. K. Kaushik. (Jialun Liu and Hojoon Ryu contributed equally to this work.) (Corresponding author: Wenjuan Zhu.) Jialun Liu is with the Department of Electrical Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA.
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - 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, A-B, 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 ∼ 30 μC/cm2 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.
AB - 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, A-B, 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 ∼ 30 μC/cm2 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.
KW - Ferroelectric hafnium oxide
KW - graphene
KW - image classifier
KW - in-memory analog computing
KW - motion detection
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U2 - 10.1109/TED.2021.3049757
DO - 10.1109/TED.2021.3049757
M3 - Article
AN - SCOPUS:85100496267
SN - 0018-9383
VL - 68
SP - 1334
EP - 1339
JO - IEEE Transactions on Electron Devices
JF - IEEE Transactions on Electron Devices
IS - 3
M1 - 9334403
ER -