Advancing clinical and nonclinical diagnostic technologies is particularly crucial for improved preparedness for the next pandemic and other global healthcare challenges. To this end, marrying advanced microscopic imaging with chip‐scale bioanalytical systems provides fresh modalities by analyzing signals of transmitted, reflected, or scattered light waves. This review brings clarity to the latest progress of refractometric imaging and biodetections on a chip, in which interferences and resonances as cornerstones of optics and plasmonics are breaking new ground. A vast range of nanophotonic and plasmonic transducers are discussed, ranging from planar films, nanoarrays, and waveguiding resonators to holistic designs. The augmented bioanalyses cover immunoassays, single‐molecule analysis, and motion tracking of bacterial pathogens and cells. Compared to single‐point spectroscopic measurements, imaging‐empowered approaches are rapidly evolving with greatly promoted signal‐noise ratio, spatial‐temporal resolutions, multiplexability, and throughput, which can be accomplished in a compact cradle system with minimized bulky components in a spectrometer‐free manner. Besides, advances in machine learning technologies applied for data analytics and transducer designs are highlighted. All in all, there are unlimited opportunities for new optical structures, principles, and ways of data retrieval to tap in, which will raise technological impacts on unveiling fundamental issues in life science and advancing global healthcare technologies.

Silicon photonics enables compact integrated photonic devices with versatile functionalities and mass manufacturing capability. However, the optimization of high-performance free-form optical devices is still challenging due to the complex light-matter interaction involved that requires time-consuming electromagnetic simulations. This problem becomes even more prominent when multiple devices are required, typically requiring separate iterative optimizations. To facilitate multi-task inverse design, we propose a topology optimization method based on deep neural network (DNN) in low-dimensional Fourier domain. The DNN takes target optical responses as inputs and predicts low-frequency Fourier components, which are then utilized to reconstruct device geometries. Removing high-frequency components for reduced design degree-of-freedom (DOF) helps control minimal features and speed up training. For demonstration, the proposed method is utilized for wavelength filter design. The trained DNN can design multiple filters instantly and concurrently with high accuracy. Totally different targets can also be further optimized through transfer learning on existing network with greatly reduced optimization rounds. Our approach can be also adapted to other free-form photonic devices, including a waveguide-coupled single-photon source that we demonstrate to prove generalizability. Such DNN-assisted topology optimization significantly reduces the time and resources required for multi-task optimization, enabling large-scale photonic device design in various applications.

No abstract