公开(公告)号：US20220121940A1

公开(公告)日：2022-04-21

申请号：US17503312

申请日：2021-10-17

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Calvin Brown ,  Artem Goncharov ,  Zachary Ballard ,  Yair Rivenson

IPC: G06N3/08 ,  G06T7/174 ,  G01J3/28

Abstract: A deep learning-based spectral analysis device and method are disclosed that employs a spectral encoder chip containing a plurality of nanohole array tiles, each with a unique geometry and, thus, a unique optical transmission spectrum. Illumination impinges upon the encoder chip and a CMOS image sensor captures the transmitted light, without any lenses, gratings, or other optical components. A spectral reconstruction neural network uses the transmitted intensities from the image to faithfully reconstruct the input spectrum. In one embodiment that used a spectral encoder chip with 252 nanohole array tiles, the network was trained on 50,352 spectra randomly generated by a supercontinuum laser and blindly tested on 14,648 unseen spectra. The system identified 96.86% of spectral peaks, with a peak localization error of 0.19 nm, peak height error of 7.60%, and peak bandwidth error of 0.18 nm.