In the context of continued spread of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and the emergence of new variants, the demand for rapid, accurate, and frequent detection is increasing. Moreover, the new predominant strain, Omicron variant, manifests more similar clinical features to those of other common respiratory infections. The concurrent detection of multiple potential pathogens helps distinguish SARS-CoV-2 infection from other diseases with overlapping symptoms, which is significant for providing tailored treatment to patients and containing the outbreak. Here, we report a lab-on-a-chip biosensing platform for SARS-CoV-2 detection based on the subwavelength grating micro-ring resonator. The sensing surface is functionalized by specific antibody against SARS-CoV-2 spike protein, which could produce redshifts of resonant peaks by antigen–antibody combination, thus achieving quantitative detection. Additionally, the sensor chip is integrated with a microfluidic chip featuring an anti-backflow Y-shaped structure that enables the concurrent detection of two analytes. In this study, we realized the detection and differentiation of COVID-19 and influenza A H1N1. Experimental results indicate that the limit of detection of our device reaches 100 fg/ml (1.31 fM) within 15 min detecting time, and cross-reactivity tests manifest the specificity of the optical diagnostic assay. Furthermore, the integrated packaging and streamlined workflow facilitate its use for clinical applications. Thus, the biosensing platform presents a promising approach for attaining highly sensitive, selective, multiplexed, and quantitative point-of-care diagnosis and distinction between COVID-19 and influenza.

Integrated photonics offers attractive solutions for realizing combinational logic for high‐performance computing. The integrated photonic chips can be further optimized using multiplexing techniques such as wavelength‐division multiplexing (WDM). In this paper, we propose a WDM‐based electronic–photonic switching network (EPSN) to realize the functions of the binary decoder and the multiplexer, which are fundamental elements in microprocessors for data transportation and processing. We experimentally demonstrate its practicality by implementing a 3–8 (three inputs, eight outputs) switching network operating at 20 Gb/s. Detailed performance analysis and performance enhancement techniques are also given in this paper.

As photonic technologies grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities for research communities. Applications include data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities continuously growing. Herein, we review state-of-the-art integrated photonic on-chip sensors that operate in the visible to mid-infrared wavelength region on various material platforms. Among the different materials, architectures, and technologies leading the way for on-chip sensors, we discuss the optical sensing principles that are commonly applied to biochemical and gas sensing. Our focus is on passive optical waveguides, including dispersion-engineered metamaterial-based structures, which are essential for enhancing the interaction between light and analytes in chip-scale sensors. We harness a diverse array of cutting-edge sensing technologies, heralding a revolutionary on-chip sensing paradigm. Our arsenal includes refractive-index-based sensing, plasmonics, and spectroscopy, which forge an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts, incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements.

Optical neural networks (ONNs) are promising hardware platforms for next‐generation neuromorphic computing due to their high parallelism, low latency, and low energy consumption. However, previous integrated photonic tensor cores (PTCs) consume numerous single‐operand optical modulators for signal and weight encoding, leading to large area costs and high propagation loss to implement large tensor operations. This work proposes a scalable and efficient optical dot‐product engine based on customized multi‐operand photonic devices, namely multi‐operand optical neuron (MOON). We experimentally demonstrate the utility of a MOON using a multi‐operand‐Mach–Zehnder‐interferometer (MOMZI) in image recognition tasks. Specifically, our MOMZI‐based ONN achieves a measured accuracy of 85.89 % in the street view house number (SVHN) recognition dataset with 4‐bit voltage control precision. Furthermore, our performance analysis reveals that a 128 × 128 MOMZI‐based PTCs outperform their counterparts based on single‐operand MZIs by one to two order‐of‐magnitudes in propagation loss, optical delay, and total device footprint, with comparable matrix expressivity.