Folded Digital Meta-Lenses for on-Chip Spectrometer

Miniature computational spectrometer with a plasmonic nanoparticles-in-cavity microfilter array

Optical spectrometers are essential tools for analysing light‒matter interactions, but conventional spectrometers can be complicated and bulky. Recently, efforts have been made to develop miniaturized spectrometers. However, it is challenging to overcome the trade-off between miniaturizing size and retaining performance. Here, we present a complementary metal oxide semiconductor image sensor-based miniature computational spectrometer using a plasmonic nanoparticles-in-cavity microfilter array. Size-controlled silver nanoparticles are directly printed into cavity-length-varying Fabry‒Pérot microcavities, which leverage strong coupling between the localized surface plasmon resonance of the silver nanoparticles and the Fabry‒Pérot microcavity to regulate the transmission spectra and realize large-scale arrayed spectrum-disparate microfilters. Supported by a machine learning-based training process, the miniature computational spectrometer uses artificial intelligence and was demonstrated to measure visible-light spectra at subnanometre resolution. The high scalability of the technological approaches shown here may facilitate the development of high-performance miniature optical spectrometers for extensive applications.

On-Chip Topological Photonic Crystal Nanobeam Filters

We present an on-chip filter with a broad tailorable working wavelength and a single-mode operation. This is realized through the application of topological photonic crystal nanobeam filters employing synthesis parameter dimensions. By introducing the translation of air holes as a new synthetic parameter dimension, we obtained nanobeams with tunable Zak phases. Leveraging the bulk-edge correspondence, we identify the existence of topological cavity modes and establish a correlation between the cavity's interface morphology and working wavelength. Through experiments, we demonstrate filters with adjustable filtering wavelengths ranging from 1301 to 1570 nm. Our work illustrates the use of the synthetic translation dimension in the design of on-chip filters, and it holds potential for applications in other devices such as microcavities.

Ultra-compact scalable spectrometer with low power consumption

An ultra-compact on-chip spectrometer was demonstrated based on an array of add-drop micro-donut resonators (MDRs). The filter array was thermally tuned by a single TiN microheater, enabling simultaneous spectral scanning across all physical channels. The MDR was designed to achieve large free spectral ranges with multimode waveguide bends and asymmetric coupling waveguides, covering a spectral range of 40 nm at the telecom waveband with five physical channels (which could be further expanded). Benefiting from the ultra-small device footprint of 150 µm2, the spectrometer achieved a low power consumption of 16 mW. Additionally, it is CMOS-compatible and enables mass fabrication, which may have potential applications in personal terminals and the consumer industry.

Review of Miniaturized Computational Spectrometers

Spectrometers are key instruments in diverse fields, notably in medical and biosensing applications. Recent advancements in nanophotonics and computational techniques have contributed to new spectrometer designs characterized by miniaturization and enhanced performance. This paper presents a comprehensive review of miniaturized computational spectrometers (MCS). We examine major MCS designs based on waveguides, random structures, nanowires, photonic crystals, and more. Additionally, we delve into computational methodologies that facilitate their operation, including compressive sensing and deep learning. We also compare various structural models and highlight their unique features. This review also emphasizes the growing applications of MCS in biosensing and consumer electronics and provides a thoughtful perspective on their future potential. Lastly, we discuss potential avenues for future research and applications.