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April 10, 2026Advanced Materials0 citationsOpen Access

Two‐Dimensional Reconfigurable Photodiode for In‐Sensor Color Filtering and Spectral Logic

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XGXiaokun GuoYZYilin ZhouYZYufeng Zhang

Key Points

  • The aim is to develop a reconfigurable photodiode for efficient spectral processing and color detection.
  • Developed a photodiode using doped tungsten diselenide (WSe2) with two switchable detection modes.
  • Evaluated the device's performance in broadband and color-filtering modes across the visible spectrum.
  • Implemented experiments for color-based logic operations and object recognition.
  • Used a convolutional neural network to classify multicolor information.
  • The device achieved a linear dynamic range of 72 dB and fast response in broadband mode.
  • Color-filtering mode allowed for detection from 445 to 780 nm with nonvolatile capabilities.
  • Simulation revealed classification accuracies of 98.99% for red and 98.76% for green patterns.

Abstract

Wavelength-selective photodetectors are essential for applications such as hyperspectral imaging, biomedical diagnostics, and secure optical communication. Conventional photodetection systems typically rely on external filters or post-processing to resolve spectral information, leading to increased system complexity and data transfer overhead. Here, we report a reconfigurable photodiode based on spatially patterned doped tungsten diselenide (WSe2), which exhibits two runtime switchable photodetection modes and a bidirectional wavelength-dependent conductance modulation across the visible spectrum. Under a broadband photodetection mode, the device exhibited a fast response and a high linear dynamic range of 72 dB. Meanwhile, under color-filtering mode, the device enables nonvolatile and color-selective detection spanning from 445 to 780 nm, to experimentally achieve the in-sensor spectral processing, including color-based logic operations and object trajectory recognition within the visible wavelength range. We further demonstrate its application in encrypted information identification using chromatically encoded digit patterns, where the device selectively decodes multicolor information via bias-controlled readout. Simulation results confirm high classification accuracies of approximately 98.99% for red and 98.76% for green patterns using a standard convolutional neural network, highlighting the potential of this platform for hardware-level spectral-domain information processing with reduced system complexity.

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Cite This Study

Guo et al. (2026) studied this question.

synapsesocial.com/papers/69d895ea6c1944d70ce0711dhttps://doi.org/10.1002/adma.72975
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