Polarization reversal enhanced intelligent recognition in two-dimensional MoTe2/GeSe heterostructure
Wide-spectral and polarization-sensitive photodetectors are vital for applications in imaging, communication, and intelligent sensing. Although two-dimensional (2D) materials have shown great promise in enhancing the performance of these devices, conventional methods for spectral discrimination ofte...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Chip |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2709472325000176 |
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| Summary: | Wide-spectral and polarization-sensitive photodetectors are vital for applications in imaging, communication, and intelligent sensing. Although two-dimensional (2D) materials have shown great promise in enhancing the performance of these devices, conventional methods for spectral discrimination often rely on complex designs, such as external filters or multisensor systems, increasing system cost and complexity. Developing simplified devices that integrate spectral and polarization detection remains a key challenge. Here, we demonstrated a 2D MoTe2/GeSe-based photodetector with wide-spectral photoresponse (400 to 1064 nm) and polarization sensitivity, achieving a responsivity of 1.35 A W−1 and a polarization ratio of 2.23 under 808 nm illumination. The device exhibited a unique 90° polarization reversal between green (532 nm) and red (808 nm), providing a novel mechanism for spectral discrimination. First-principles calculations reveal the polarization reversal phenomenon based on the heterostructure's optical anisotropy. Furthermore, integration with a convolutional neural network enables intelligent traffic signal recognition using polarization-sensitive images. This work highlights the potential of MoTe2/GeSe heterostructures for next-generation photodetectors, offering compact, multifunctional solutions with integrated spectral and polarization discrimination capabilities. |
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| ISSN: | 2709-4723 |