Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing

Jia Zhou; Wen Li; Ye Chen; Haowen Qian; Yen-Hung Lin; Ruipeng Li; Zhen Wang; Jin Wang; Wei Shi; Xianwang Tao; Youtian Tao; Haifeng Ling; Wei Huang; Mingdong Yi

doi:10.1038/s41377-025-01986-9

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Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing

Original Articles

- Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing
- Light: Science & Applications Vol. 14, Issue 10, Pages: 3250-3260(2025)
- Affiliations：
  
  1.State Key Laboratory of Flexible Electronics, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing 210023, China
  2.Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
  3.State Key Laboratory of Advanced Displays and Optoelectronics Technologies, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
  4.National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY 11973, USA
  5.Key Lab for Flexible Electronics and Institute of Advanced Materials, Nanjing Tech University, Nanjing 211816, China
- Author bio：
  
  Wen Li (iamwli@njupt.edu.cn)
  Youtian Tao (iamyttao@njtech.edu.cn)
  Wei Huang (vc@nwpu.edu.cn)
  Mingdong Yi (iammdyi@njupt.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-01986-9
  CLC：
- Received：18 February 2025，
  
  Revised：2025-07-09，
  
  Accepted：24 July 2025，
  
  Published Online：08 September 2025，
  
  Published：31 October 2025
- Accepted：
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Zhou, J. et al. Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing. Light: Science & Applications, 14, 3250-3260 (2025).
DOI：

Zhou, J. et al. Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing. Light: Science & Applications, 14, 3250-3260 (2025). DOI： 10.1038/s41377-025-01986-9.

摘要

Abstract

As the demand for edge platforms in artificial intelligence increases

including mobile devices and security applications

the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here

we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic “control-on-demand” architecture. Integrating signal sensing

featuring

and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module

and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore

we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform

we propose a resilient and adaptable framework for energy-efficient and economical edge computing.

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references

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