In-memory ferroelectric differentiator

Guangdi Feng, Xiaoming Zhao, Xiaoyue Huang, Xiaoxu Zhang, Yangyang Wang, Wei Li, Luqiu Chen, Shenglan Hao, Qiuxiang Zhu, Yachin Ivry, Brahim Dkhil, Bobo Tian (), Peng Zhou (), Junhao Chu and Chungang Duan ()
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Guangdi Feng: East China Normal University
Xiaoming Zhao: East China Normal University
Xiaoyue Huang: East China Normal University
Xiaoxu Zhang: East China Normal University
Yangyang Wang: East China Normal University
Wei Li: East China Normal University
Luqiu Chen: East China Normal University
Shenglan Hao: East China Normal University
Qiuxiang Zhu: East China Normal University
Yachin Ivry: Technion-Israel Institute of Technology
Brahim Dkhil: Laboratoire SPMS
Bobo Tian: East China Normal University
Peng Zhou: Fudan University
Junhao Chu: East China Normal University
Chungang Duan: East China Normal University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Differential calculus is the cornerstone of many disciplines, spanning the breadth of modern mathematics, physics, computer science, and engineering. Its applications are fundamental to theoretical progress and practical solutions. However, the current state of digital differential technology often requires complex implementations, which struggle to meet the extensive demands of the ubiquitous edge computing in the intelligence age. To face these challenges, we propose an in-memory differential computation that capitalizes on the dynamic behavior of ferroelectric domain reversal to efficiently extract information differences. This strategy produces differential information directly within the memory itself, which considerably reduces the volume of data transmission and operational energy consumption. We successfully illustrate the effectiveness of this technique in a variety of tasks, including derivative function solving, the moving object extraction and image discrepancy identification, using an in-memory differentiator constructed with a crossbar array of 1600-unit ferroelectric polymer capacitors. Our research offers an efficient hardware analogue differential computing, which is crucial for accelerating mathematical processing and real-time visual feedback systems.

Date: 2025
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DOI: 10.1038/s41467-025-58359-4

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