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.
- Ferroelectric hafnium oxide
- image classifier
- in-memory analog computing
- motion detection
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering