光探测
光电探测器
分光计
异质结
光电子学
光学
材料科学
非线性系统
光功率
物理
激光器
量子力学
作者
Rana Darweesh,Rajesh Kumar Yadav,E.L. Adler,Michal Poplinger,Adi Levi,Jea-Jung Lee,Amir Leshem,Ashwin Ramasubramaniam,Fengnian Xia,Doron Naveh
出处
期刊:Science Advances
[American Association for the Advancement of Science (AAAS)]
日期:2024-05-17
卷期号:10 (20)
标识
DOI:10.1126/sciadv.adn6028
摘要
Computational spectrometry is an emerging field that uses photodetection in conjunction with numerical algorithms for spectroscopic measurements. Compact single photodetectors made from layered materials are particularly attractive since they eliminate the need for bulky mechanical and optical components used in traditional spectrometers and can easily be engineered as heterostructures to optimize device performance. However, such photodetectors are typically nonlinear devices, which adds complexity to extracting optical spectra from their response. Here, we train an artificial neural network to recover the full nonlinear spectral photoresponse of a single GeSe-InSe p-n heterojunction device. The device has a spectral range of 400 to 1100 nm, a small footprint of ~25 × 25 square micrometers, and a mean reconstruction error of 2 × 10 −4 for the power spectrum at 0.35 nanometers. Using our device, we demonstrate a solution to metamerism, an apparent matching of colors with different power spectral distributions, which is a fundamental problem in optical imaging.
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