In this article, we review our recent work on mid-infrared (mid-IR) photonic materials and devices fabricated on silicon for on-chip sensing applications. Pedestal waveguides based on silicon are demonstrated as broadband mid-IR sensors. Our low-loss mid-IR directional couplers demonstrated in SiNx waveguides are useful in differential sensing applications. Photonic crystal cavities and microdisk resonators based on chalcogenide glasses for high sensitivity are also demonstrated as effective mid-IR sensors. Polymer-based functionalization layers, to enhance the sensitivity and selectivity of our sensor devices, are also presented. We discuss the design of mid-IR chalcogenide waveguides integrated with polycrystalline PbTe detectors on a monolithic silicon platform for optical sensing, wherein the use of a low-index spacer layer enables the evanescent coupling of mid-IR light from the waveguides to the detector. Finally, we show the successful fabrication processing of our first prototype mid-IR wa...

While the vast majority of integrated photonic devices are traditionally fabricated on rigid substrates, photonic integration of both passive and active photonic devices on flexible polymer substrates has been demonstrated in recent years, and its applications in imaging, sensing and optical interconnects are being actively pursued. This paper presents an overview of the emerging field of mechanically flexible photonics, where we examine material processing and mechanical design rationales dictated by application-specific optical functionalities. The examples include semiconductor nanomembranes which serve as the key enabling material for hybrid inorganic-organic flexible active photonics, and monolithically integrated passive photonic structures fabricated from semiconductors, polymers, or amorphous materials. Technical challenges and further research opportunities related to materials engineering and device integration on flexible substrates are also discussed.

In recent years, neural networks based on central processing units (CPUs) have been used in mobile phones for speech recognition and image classification,¹ but they are still in their infancy in more sophisticated and expansive application fields where massive amounts of data should be processed in real time, such as autonomous driving² and computer vision.³ Optical neural networks (ONNs) based on photonic integrated circuits (PICs)^{4 – 9} have the potential to meet this demand as a consequence of their low latency, high parallel (e.g., wavelength/spatial division multiplexing), and strong anti-electromagnetic interference capability of PICs, as well as the low cost and high yield provided by a complementary metal-oxide-semiconductor (CMOS) fabrication process.^{10 – 13} Recently, a series of ONNs have been demonstrated for artificial intelligence, including vowel recognition,¹⁴ perceptron,^15,16 pattern recognition,¹⁷ and image classification.^18,19 However, for real-world applications, more efforts are needed to improve the energy efficiency, scalability, and algorithm accuracy of ONNs.

We demonstrated high-index-contrast, waveguide-coupled ${\mathrm{As}}_2{\mathrm{Se}}_3$ chalcogenide glass resonators monolithically integrated on silicon fabricated using optical lithography and a lift-off process. The resonators exhibited a high intrinsic quality factor of $2\times {10}^5$ at 5.2 μm wavelength, which is among the highest values reported in on-chip mid-infrared (mid-IR) photonic devices. The resonator can serve as a key building block for mid-IR planar photonic circuits.