Low-power edge detection based on ferroelectric field-effect transistor

Jiajia Chen, Jiacheng Xu, Jiani Gu (), Bowen Chen, Hongrui Zhang, Haoji Qian, Huan Liu, Rongzong Shen, Gaobo Lin, Xiao Yu, Miaomiao Zhang, Yi’an Ding, Yan Liu, Jianshi Tang, Huaqiang Wu, Chengji Jin () and Genquan Han
Additional contact information
Jiajia Chen: Xidian University
Jiacheng Xu: Zhejiang Lab
Jiani Gu: Zhejiang Lab
Bowen Chen: Zhejiang Lab
Hongrui Zhang: Xidian University
Haoji Qian: Xidian University
Huan Liu: Xidian University
Rongzong Shen: Zhejiang Lab
Gaobo Lin: Zhejiang Lab
Xiao Yu: Xidian University
Miaomiao Zhang: Xidian University
Yi’an Ding: Xidian University
Yan Liu: Xidian University
Jianshi Tang: Tsinghua University
Huaqiang Wu: Tsinghua University
Chengji Jin: Xidian University
Genquan Han: Xidian University

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Edge detection is one of the most essential research hotspots in computer vision and has a wide variety of applications, such as image segmentation, target detection, and other high-level image processing technologies. However, efficient edge detection is difficult in a resource-constrained environment, especially edge-computing hardware. Here, we report a low-power edge detection hardware system based on HfO2-based ferroelectric field-effect transistor, which is one of the most potential non-volatile memories for energy-efficient computing. Different from the conventional edge detectors requiring sophisticated hardware for the complex operation such as convolution and gradient, the proposed edge detector is analogue-to-digital converter free and loaded into a multi-bit content addressable memory, which only needs one 4 × 4 ferroelectric field-effect transistor NAND array. The experimental results show that the proposed hardware system is able to achieve efficient image edge detection at low power consumption (~10 fJ/per operation), realizing no-accuracy-loss, low-power and analogue-to-digital-converter-free hardware system, providing a feasible solution for edge computing.

Date: 2025
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DOI: 10.1038/s41467-024-55224-8

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