| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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Memristor-based analog noise correction for infrared sensors |
| Xiao Huang(黄潇)†, Peiwen Tong(童霈文)†, Qingjiang Li(李清江), Tuo Ma(马拓), Shuo Han(韩硕), Wei Wang(王伟)‡, and Yi Sun(孙毅)§ |
| College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China |
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Abstract Sensor noise is a critical factor that degrades the performance of image processing systems. In traditional computing systems, noise correction is implemented in the digital domain, resulting in redundant latency and power consumption overhead in the analog-to-digital conversion. In this work, we propose an analog-domain image correction architecture based on a proposed small-scale UNet, which implements a compact noise correction network within a one-transistor-one-memristor (1T1R) array. The statistical non-idealities of the fabricated 1T1R array (e.g., device variability) are rigorously incorporated into the network's training and inference simulations. This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise, defective pixels (stuck-at-bright/dark), and exposure mismatch. Compared to systems without correction, the proposed architecture achieves up to 50.13 % improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors. The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture. This work establishes a new paradigm for advancing the development of low-power, low-latency, and high-precision image processing systems.
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Received: 15 April 2025
Revised: 12 June 2025
Accepted manuscript online: 08 July 2025
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PACS:
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85.35.-p
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(Nanoelectronic devices)
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84.37.+q
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(Measurements in electric variables (including voltage, current, resistance, capacitance, inductance, impedance, and admittance, etc.))
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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31.30.i
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| Fund: Project supported by the National Key Research and Development Program of China (Grant No. 2024YFA1208800) and the National Natural Science Foundation of China (Grant Nos. 62404253, 62304254, and U23A20322). |
Corresponding Authors:
Wei Wang, Yi Sun
E-mail: wangwei_esss@nudt.edu.cn;sunyi12@nudt.edu.cn
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Cite this article:
Xiao Huang(黄潇), Peiwen Tong(童霈文), Qingjiang Li(李清江), Tuo Ma(马拓), Shuo Han(韩硕), Wei Wang(王伟), and Yi Sun(孙毅) Memristor-based analog noise correction for infrared sensors 2026 Chin. Phys. B 35 028501
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[1] Irie K, McKinnon A E, Unsworth K and Woodhead I M 2008 IEEE Trans. Circuits Syst. Video Technol. 18 28
[2] Bu F, Yao D, Yang Y and Cao W 2023 Third International Conference on Optics and Image Processing, April 7–9, 2023, Xi’an, China, pp. 121–127
[3] Chen S, Lou Z, Chen D and Shen G 2018 Adv. Mater. 30 1705400
[4] DuW, Li C, Huang Y, Zou J, Luo L, Teng C, Kuo H C,Wu J andWang Z 2022 IEEE Electron Dev. Lett. 43 406
[5] Liu K, Zhang T, Dang B, Bao L, Xu L, Cheng C, Yang Z, Huang R and Yang Y 2022 Nat. Electron. 5 761
[6] Sun Y, Li Q, Zhu X, Liao C, Wang Y, Li Z, Liu S, Xu H and Wang W 2023 Adv. Intell. Syst. 5 2200196
[7] Dang B, Liu K, Wu X, Yang Z, Xu L, Yang Y and Huang R 2023 Adv. Mater. 35 2204844
[8] Zhou G, Li J, Song Q, Wang L, Ren Z, Sun B, Hu X, Wang W, Xu G, Chen X, Cheng L, Zhou F and Duan S 2023 Nat. Commun. 14 8489
[9] Zhan Y, Ding M, Xiao F and Zhang X 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation, December 14–17, 2011, Wuhan, China, pp. 31–34
[10] Dabov K, Foi A, Katkovnik V and Egiazarian K 2007 IEEE Trans. Image Process. 16 2080
[11] Burger H C, Schuler C J and Harmeling S 2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16–21, 2012, Providence, Rhode Island, pp. 2392–2399
[12] Zhang K, Zuo W, Chen Y, Meng D and Zhang L 2017 IEEE Trans. Image Process. 26 3142
[13] Xia Q and Yang J J 2019 Nat. Mater. 18 309
[14] Ielmini D and Wong H S P 2018 Nat. Electron. 1 333
[15] Ronneberger O, Fischer P and Brox T 2015 Medical Image Computing and Computer-Assisted Intervention, October 5–9, 2015, Munich Germany, pp. 234–241
[16] Chen J Y, Liu X, Du L L, Song B and Sun X B 2024 Acta Optica Sinica 44 375 (in Chinese)
[17] Posso J, Kieffer H, Menga N, Hlimi O, Tarris S, Guerard H, Bois G, Couderc M and Jenn E 2025 Real-Time Semantic Segmentation of Aerial Images Using an Embedded U-Net: A Comparison of CPU, GPU, and FPGA Workflows
[18] Liu Z, Tang J, Gao B, Yao P, Li X, Liu D, Zhou Y, Qian H, Hong B and Wu H 2020 Nat. Commun. 11 4234 |
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