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In situ spectral reconstruction based on a memristor chip for energy-efficient computational spectrometry

Nature Electronics (2026)Cite this article

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Abstract

Computational spectrometers, which rely on reconstruction algorithms to decode spectral information from raw sensor data, are of potential use in portable, in-field spectrometry. However, research on such systems primarily focuses on the front-end encoding devices, and back-end decoding hardware remains limited by severe overheads. Here we report an in situ computational spectrometer implemented on a fully integrated 576-Kb memristor chip. With systematic robustness analysis, we develop memristive regularization and filter embedding strategies to overcome the extreme sensitivity of ill-posed spectral reconstruction, achieving software-equivalent accuracy. System-level benchmarking shows that our hardware takes only 125.0 ns to reconstruct one spectrum consuming 6.7 nJ of energy, which is 26.5 times faster and 162.7 times more energy-efficient than state-of-the-art computational spectrometers. Our work illustrates the potential of memristor-chip-based computational spectrometry and provides approaches for efficiently implementing signal processing algorithms on memristor chips.

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Fig. 1: Schematic of MICS.
Fig. 2: Memristor-chip-based decoding hardware and its robustness analysis.
Fig. 3: MRG strategy and spectral reconstruction results.
Fig. 4: FEM strategy for noise effect suppression.
Fig. 5: Demonstration of hyperspectral imaging with MICS.
Fig. 6: Performance benchmark of the MICS.

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Data availability

Source data for Figs. 3–5 are available via GitHub at https://github.com/Tsinghua-LEMON-Lab/Memristor-computational-spectrometer. Additional data are available from the corresponding authors upon reasonable request.

Code availability

Source code is available via GitHub at https://github.com/Tsinghua-LEMON-Lab/Memristor-computational-spectrometer. Additional codes of this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

This work was supported in part by the Brain Science and Brain-like Intelligence Technology—National Science and Technology Major Project 2022ZD0210200 (J.T.); National Natural Science Foundation of China 92264201 (J.T.), 52061135108 (W.C.), 62025111 (H.W.) and 624B2078 (X.L.); the XPLORER Prize (H.W.); Scientific Research Innovation Capability Support Project for Young Faculty ZYGXQNJSKYCXNLZCXM-E8 (W.C.); and the Center of Nanofabrication, Tsinghua University.

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Author notes

  1. These authors contributed equally: Han Zhao, Lei Wang, Yanze Zhou.

Authors and Affiliations

  1. School of Integrated Circuits, Beijing Advanced Innovation Center for Integrated Circuits, Tsinghua University, Beijing, China

    Han Zhao, Yanze Zhou, Siqi Liu, Qi Qin, Xueqi Li, Yue Xi, Yunrui Jiao, Zhengwu Liu, Ruofei Hu, Yudeng Lin, Peng Yao, Bin Gao, He Qian, Jianshi Tang & Huaqiang Wu

  2. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Lei Wang, Yiru Zhang, Xuewei Feng, Yingzheng Liu & Weiwei Cai

  3. State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

    Liangjun Lu

  4. Department of Engineering, University of Cambridge, Cambridge, UK

    Tawfique Hasan

  5. QTF Centre of Excellence, Department of Electronics and Nanoengineering, Aalto University, Espoo, Finland

    Zhipei Sun

  6. Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China

    Bin Gao, He Qian, Jianshi Tang & Huaqiang Wu

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Contributions

W.C. and J.T. conceived the idea and supervised the research. L.W., H.Z. and Y. Zhou analysed the algorithms’ robustness and proposed the MRG strategy. H.Z., L.W. and Y. Zhou proposed the FEM strategies and designed the experiments. H.Z., L.W. and Y. Zhou performed the experiments with help from Q.Q., X.L., Y. Zhang, S.L., Y.X., Y.J., Z.L., R.H., Y. Lin, X.F., L.L., T.H., Z.S., Y. Liu, P.Y., B.G., H.Q. and H.W. All authors discussed the results and commented on the paper.

Corresponding authors

Correspondence to Jianshi Tang, Weiwei Cai or Huaqiang Wu.

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The authors declare no competing interests.

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Nature Electronics thanks Suhas Kumar, Yiyu Shi and Suin Yi for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Notes 1–13, Figs. 1–31 and Tables 1–6.

Supplementary Video 1 (download MP4 )

Spectral imaging results reconstructed by MICS. This video shows the detailed spectral imaging results reconstructed by MICS. It contains 61 frames of different spectra, with a comparison of the ground truth.

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Zhao, H., Wang, L., Zhou, Y. et al. In situ spectral reconstruction based on a memristor chip for energy-efficient computational spectrometry. Nat Electron (2026). https://doi.org/10.1038/s41928-026-01571-x

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