Efficient light energy transfer between optical waveguides has been a critical issue in various areas of photonics and optoelectronics. Especially, the light coupling between optical fibers and integrated waveguide structures provides essential input-output interfaces for photonic integrated circuits (PICs) and plays a crucial role in reliable optical signal transport for a number of applications, such as optical interconnects, optical switching, and integrated quantum optics. Significant efforts have been made to improve light coupling properties, including coupling efficiency, bandwidth, polarization dependence, alignment tolerance, as well as packing density. In this review article, we survey three major light coupling methods between optical fibers and integrated waveguides: end-fire coupling, diffraction grating-based coupling, and adiabatic coupling. Although these waveguide coupling methods are different in terms of their operating principles and physical implementations, they have gradually adopted various nanophotonic structures and techniques to improve the light coupling properties as our understanding to the behavior of light and nano-fabrication technology advances. We compare the pros and cons of each light coupling method and provide an overview of the recent developments in waveguide coupling between optical fibers and integrated photonic circuits.

Fabric-based electronic textiles (e-textiles) are the fundamental components of wearable electronic systems, which can provide convenient hand-free access to computer and electronics applications. However, e-textile technologies presently face significant technical challenges. These challenges include difficulties of fabrication due to the delicate nature of the materials, and limited operating time, a consequence of the conventional normally on computing architecture, with volatile power-hungry electronic components, and modest battery storage. Here, we report a novel poly(ethylene glycol dimethacrylate) (pEGDMA)-textile memristive nonvolatile logic-in-memory circuit, enabling normally off computing, that can overcome those challenges. To form the metal electrode and resistive switching layer, strands of cotton yarn were coated with aluminum (Al) using a solution dip coating method, and the pEGDMA was conformally applied using an initiated chemical vapor deposition process. The intersection of two Al/pEGDMA coated yarns becomes a unit memristor in the lattice structure. The pEGDMA-Textile Memristor (ETM), a form of crossbar array, was interwoven using a grid of Al/pEGDMA coated yarns and untreated yarns. The former were employed in the active memristor and the latter suppressed cell-to-cell disturbance. We experimentally demonstrated for the first time that the basic Boolean functions, including a half adder as well as NOT, NOR, OR, AND, and NAND logic gates, are successfully implemented with the ETM crossbar array on a fabric substrate. This research may represent a breakthrough development for practical wearable and smart fibertronics.

Fabric‐based electronic textiles (e‐textiles) have been investigated for the fabrication of high‐performance wearable electronic devices with good durability. Current e‐textile technology is limited by not only the delicate characteristics of the materials used but also by the fabric substrates, which impose constraints on the fabrication process. A polydopamine (PDA)‐intercalated fabric memory (PiFAM) with a resistive random access memory (RRAM) architecture is reported for fabric‐based wearable devices, as a step towards promising neuromorphic devices beyond the most simple. It is composed of interwoven cotton yarns. A solution‐based dip‐coating method is used to create a functional core–shell yarn. The outer shell is coated with PDA and the inner shell is coated with aluminum (Al) surrounding the core yarn, which serves as a backbone. The Al shell serves as the RRAM electrode and the PDA is a resistive‐switching layer. These functional yarns are then interwoven to create the RRAM in a lattice point. Untreated yarn is intercalated between adjacent functional yarns to avoid cell‐to‐cell interference. The PiFAM is applied to implement a synapse, and the feasibility of a neuromorphic device with pattern recognition accuracy of ≈81% and the potential for application in wearable and flexible electronic platforms is demonstrated.

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