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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Han Zhao,
  • Lei Wang,
  • Yanze Zhou,
  • Siqi Liu,
  • Qi Qin,
  • Xueqi Li,
  • Yiru Zhang,
  • Yue Xi,
  • Yunrui Jiao,
  • Zhengwu Liu,
  • Ruofei Hu,
  • Yudeng Lin,
  • Xuewei Feng,
  • Liangjun Lu,
  • Tawfique Hasan,
  • Zhipei Sun,
  • Yingzheng Liu,
  • Peng Yao,
  • Bin Gao,
  • He Qian,
  • Jianshi Tang,
  • Weiwei Cai,
  • Huaqiang Wu

摘要

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.