{"id":773,"date":"2025-02-28T10:57:00","date_gmt":"2025-02-28T02:57:00","guid":{"rendered":"https:\/\/frontbasic.sslab.org.cn\/lin-group\/?post_type=paper&#038;p=773"},"modified":"2025-07-02T11:00:11","modified_gmt":"2025-07-02T03:00:11","slug":"sns2-wse2-van-der-waals-single-detector-spectrometer-with-a-dynamically-selecting-spectral-reconstruction-strategy","status":"publish","type":"paper","link":"https:\/\/frontbasic.sslab.org.cn\/lin-group\/paper\/sns2-wse2-van-der-waals-single-detector-spectrometer-with-a-dynamically-selecting-spectral-reconstruction-strategy\/","title":{"rendered":"SnS2\/WSe2 van der Waals single-detector spectrometer with a dynamically selecting spectral reconstruction strategy"},"content":{"rendered":"\n<p><a href=\"https:\/\/ieeexplore.ieee.org\/document\/10904467\">SnS\u2082\/WSe\u2082 van der Waals Single-Detector Spectrometer With a Dynamically Selecting Spectral Reconstruction Strategy | IEEE Journals &amp; Magazine | IEEE Xplore<\/a><\/p>\n\n\n\n<p>The single-detector spectrometers based on 2D layer van der Waals (vdW) heterojunctions offer advantages in spectral reconstruction due to their high sensitivity, tunable optical properties, and the ability to cover a broad spectral range. There exist two principal algorithms dominating spectrum reconstruction for this kind spectrometer: the Tikhonov regularization method combined with the Least Squares Method (LSM) and neural network-based approaches, particularly Deep Learning (DL). However, both of the algorithms exhibit inherent limitations in spectral reconstruction, which constrain the versatility of computational spectrometers that rely solely on a single algorithm for reconstructing diverse spectral profiles. To overcome this limitation, we introduce an artificial neural network (ANN)-based classification model capable of dynamically selecting the optimal algorithm throughout the reconstruction process. This enables highly accurate spectral reconstruction within the 440-700 nm wavelength range, achieving a spectral resolution of 6 nm. By harnessing the complementary strengths of multiple algorithms, our approach proposes a novel strategy for combining techniques to enhance the precision of spectral reconstructions, laying the groundwork for more sophisticated methods in the future.<br \/><\/p>\n","protected":false},"featured_media":774,"template":"","acf":[],"_links":{"self":[{"href":"https:\/\/frontbasic.sslab.org.cn\/lin-group\/wp-json\/wp\/v2\/paper\/773"}],"collection":[{"href":"https:\/\/frontbasic.sslab.org.cn\/lin-group\/wp-json\/wp\/v2\/paper"}],"about":[{"href":"https:\/\/frontbasic.sslab.org.cn\/lin-group\/wp-json\/wp\/v2\/types\/paper"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/frontbasic.sslab.org.cn\/lin-group\/wp-json\/wp\/v2\/media\/774"}],"wp:attachment":[{"href":"https:\/\/frontbasic.sslab.org.cn\/lin-group\/wp-json\/wp\/v2\/media?parent=773"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}