AI-Augmented Photon-Trapping Spectrometer-on-a-Chip on Silicon Platform with Extended Near-Infrared Sensitivity
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918129509072896 |
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| author | Ahamed, Ahasan Myat, Htet Rawat, Amita McPhillips, Lisa N Islam, M Saif |
| author_facet | Ahamed, Ahasan Myat, Htet Rawat, Amita McPhillips, Lisa N Islam, M Saif |
| contents | We present a compact, noise-resilient reconstructive spectrometer-on-a-chip that achieves high-resolution hyperspectral imaging across an extended near-infrared (NIR) range up to 1100nm. The device integrates monolithically fabricated silicon photodiodes enhanced with photon-trapping surface textures (PTST), enabling improved responsivity in the low-absorption NIR regime. Leveraging a fully connected neural network, we demonstrate accurate spectral reconstruction from only 16 uniquely engineered detectors, achieving <0.05 RMSE and 8nm resolution over a wide spectral range of 640nm to 1100nm. Our system outperforms conventional spectrometers, maintaining signal-to-noise ratio above 30dB even with 40dB of added detector noise; extending functionality to longer wavelengths up to 1100nm, while the traditional spectrometers fail to perform beyond 950nm due to poor detector efficiency and noise performance. With a footprint of 0.4mm2, dynamic range of 50dB, ultrafast time response (57ps), and high photodiode gain (>7000), this AI-augmented silicon spectrometer is well-suited for portable, real-time, and low-light applications in biomedical imaging, environmental monitoring, and remote sensing. The results establish a pathway toward fully integrated, high-performance hyperspectral sensing in a CMOS-compatible platform. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_13521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Augmented Photon-Trapping Spectrometer-on-a-Chip on Silicon Platform with Extended Near-Infrared Sensitivity Ahamed, Ahasan Myat, Htet Rawat, Amita McPhillips, Lisa N Islam, M Saif Optics Signal Processing Computational Physics We present a compact, noise-resilient reconstructive spectrometer-on-a-chip that achieves high-resolution hyperspectral imaging across an extended near-infrared (NIR) range up to 1100nm. The device integrates monolithically fabricated silicon photodiodes enhanced with photon-trapping surface textures (PTST), enabling improved responsivity in the low-absorption NIR regime. Leveraging a fully connected neural network, we demonstrate accurate spectral reconstruction from only 16 uniquely engineered detectors, achieving <0.05 RMSE and 8nm resolution over a wide spectral range of 640nm to 1100nm. Our system outperforms conventional spectrometers, maintaining signal-to-noise ratio above 30dB even with 40dB of added detector noise; extending functionality to longer wavelengths up to 1100nm, while the traditional spectrometers fail to perform beyond 950nm due to poor detector efficiency and noise performance. With a footprint of 0.4mm2, dynamic range of 50dB, ultrafast time response (57ps), and high photodiode gain (>7000), this AI-augmented silicon spectrometer is well-suited for portable, real-time, and low-light applications in biomedical imaging, environmental monitoring, and remote sensing. The results establish a pathway toward fully integrated, high-performance hyperspectral sensing in a CMOS-compatible platform. |
| title | AI-Augmented Photon-Trapping Spectrometer-on-a-Chip on Silicon Platform with Extended Near-Infrared Sensitivity |
| topic | Optics Signal Processing Computational Physics |
| url | https://arxiv.org/abs/2508.13521 |