Spectral analysis of light is one of the oldest and most versatile scientific methods and the basis of countless techniques and instruments. Miniaturized spectrometers have recently seen great advances, but challenges remain before they are widely deployed. We report an integrated photonic spectrometer that achieves high performance with minimal component complexity by combining imaging of light propagation patterns in multi-mode interference waveguides with machine learning analysis. We demonstrate broadband operation in the visible and near-infrared, 0.05 nm spectral resolution, and an array of four spectrometers on a single chip. Two canonical applications are implemented: spectral analysis of the solar spectrum with neural network reconstruction and detection of Rayleigh scattering from microbeads on an optofluidic chip using principal component classification. These results illustrate the potential of this approach for high-performance spectroscopy across disciplines.

Integrated optofluidic biosensors can fill the need for sensitive, amplification-free, multiplex single molecule detection which is relevant for containing the spread of infectious diseases such as COVID-19. Here, we demonstrate a rapid sample-to-answer scheme that uses a field programmable gate array (FPGA) to enable live monitoring of single particle fluorescence analysis on an optofluidic chip. Fluorescent nanobeads flowing through a micro channel are detected with 99% accuracy and particle concentrations in clinically relevant ranges from 3.4x10 ⁴ to 3.4 × 10 ⁶ /ml are determined within seconds to a few minutes without the need for post-experiment data extraction and analysis. In addition, other extract salient experimental parameters such as dynamic flow rate changes can be monitored in real time. The sensor is validated with real-time fluorescence detection of single bacterial plasmid DNA at attomolar concentrations, showing excellent promise for implementation as a point of care (POC) diagnostic tool.

Chip-scale integrated imaging spectrometers show significant potential for high-performance spectral analysis due to advancements in fabrication and computational techniques. Many practical applications, such as astronomy and molecular spectroscopy, require analyzing light at sub-nanowatt levels, where inherent enhancement in spectrometer signals can reduce the need for expensive photodetectors or long integration time. Previously, we introduced an integrated spectrometer scheme using machine learning to reconstruct spectra from imaging the wavelength-dependent patterns scattered out of a multimode interference (MMI) waveguide. In this work, we report a signal enhancement of 13.6 dB and an increase of device sensitivity and dynamic range by 15 dB by selective roughening of the waveguide surface via plasma etching. By imaging interference patterns at various points along the waveguide, we determine that the best spectrometer performance is achieved by imaging MMI sections with highest pattern variation. We report accurate spectral measurements using convolutional neural network-based spectral reconstruction with 1 nm resolution at input powers as low as 300 pW for the present experimental configuration, and a scattering coefficient of 1.109 cm^-1 from the etched section.

Optofluidic sensors have enabled single molecule sensing using planar, waveguide dependent multi-spot fluorescence excitation. Here, we demonstrate a new approach to single-particle fluorescence sensing using free-space, top-down illumination of liquid-core antiresonant reflecting optical waveguide (ARROW) devices using two different multi-spot excitation techniques. First, the liquid core ARROW waveguide is excited with a focused beam through a slit pattern milled into an opaque aluminum film, showing comparable performance for single bead fluorescence detection as in-plane, multi-mode interference waveguide based excitation. The second top-down illumination technique images the spot pattern from a Y-splitter SiO ₂ waveguide chip directly onto the detection device for efficient power utilization and circumventing the need for an opaque cover, producing a further $2.7\times $ improvement in signal-to-noise ratio. The two top-down approaches open up new possibilities for chip-based optical particle sensing with relaxed alignment tolerances.

SPIE Digital Library Proceedings

We present a model and simulation for predicting the detected signal of a fluorescence-based optical biosensor built from optofluidic waveguides. Typical applications include flow experiments to determine pathogen concentrations in a biological sample after tagging relevant DNA or RNA sequences. An overview of the biosensor geometry and fabrication processes is presented. The basis for the predictive model is also outlined. The model is then compared to experimental results for three different biosensor designs. The model is shown to have similar signal statistics as physical tests, illustrating utility as a pre-fabrication design tool and as a predictor of detection sensitivity.