On‐Chip Near‐Infrared Spectral Sensing with Minimal Plasmon‐Modulated Channels

Abstract Spectroscopy is widely used in biomedicine, agriculture, remote sensing, industrial process monitoring, etc. The emerging on‐chip integration techniques further extend these applications to on‐site and portable operating modes. However, realizing high‐applicability and low‐cost spectroscopy chips is still a major challenge particularly in infrared due to the requirements on a large number of spectral channels. Here, a compact and low‐cost near‐infrared spectral sensing platform is demonstrated, releasing the burden of scaling up the spectral channel and simultaneously keeping a high accuracy in chemical classification. The monolithically integrated spectral sensor consists of a small amount of plasmon‐modulated narrowband photodetection units and operates with on‐chip photoelectric responses via a statistical machine learning method. Both plasmonic resonances with low spectral correlation and advanced regression algorithms contribute to high sensing accuracy in the case of limited hardware resources. Chemical concentration quantification, plastic sorting, and scanning spectral imaging are realized using such a sensor with up to twelve spectral channels. This concept avoids the uncertainty of various spectral reconstruction algorithms, high cost, and processing complexity and therefore provides promising strategies for miniaturized spectral sensing platforms.