arXiv subjects

Shaoliang Yu

Publications and source records attributed to Shaoliang Yu.

5 recordsLinked to original sources

arXiv2025

Suspended waveguide-enhanced near-infrared photothermal spectroscopy for ppb-level molecular gas sensing on a chalcogenide chip

On-chip waveguide sensors have attracted significant attention recently due to their potential for high level integration. However, so far on-chip gas sensing based on traditional laser absorption spectroscopy has demonstrated low detection sensitivity, due to weak light-gas interaction over a limited interaction distance. On-chip photothermal spectroscopy (PTS) appears to be a powerful technique to achieve higher sensitivity, its performance is yet constrained to parts-per-million (ppm)-level due to small fraction of evanescent field in the light-gas interaction zone and fast thermal dissipation through the solid substrate. Herein, we demonstrated suspended chalcogenide glass waveguide (ChGW)-enhanced PTS that overcomes these limitations, enabling highly sensitive parts-per-billion (ppb)-level molecular gas sensing. We fabricated a nanoscale suspended ChGW with low loss of 2.6 dB/cm using CMOS-compatible two-step patterning process. By establishing an equivalent PTS model to guide the optimization of the ChGW geometry, we achieved a 4-fold increase in the absorption-induced heat source power and a 10.6-fold decrease in the equivalent heat conductivity, resulting in a 45-fold enhancement in photothermal phase modulation efficiency over the non-suspended waveguides. Combining with a high-contrast waveguide facet-formed Fabry-Perot interferometer, we achieved an unprecedented acetylene detection limit of 330 ppb, a large dynamic range close to 6 orders of magnitude, and a fast response of less than 1 s. The overall system exhibits a noise-equivalent absorption coefficient of 3.8x10-7 cm-1, setting a new benchmark for photonic waveguide gas sensors to the best of our knowledge. This work provides a key advancement towards prototyping an integrated sensor-on-a-chip for highly sensitive and background-free photonic sensing applications.

physics.optics↗

arXiv2024

Optical Spiking Neurons Enable High-Speed and Energy-Efficient Optical Neural Networks

Optical neural networks (ONNs) perform extensive computations using photons instead of electrons, resulting in passively energy-efficient and low-latency computing. Among various ONNs, the diffractive optical neural networks (DONNs) particularly excel in energy efficiency, bandwidth, and parallelism, therefore attract considerable attention. However, their performance is limited by the inherent constraints of traditional frame-based sensors, which process and produce dense and redundant information at low operating frequency. Inspired by the spiking neurons in human neural system, which utilize a thresholding mechanism to transmit information sparsely and efficiently, we propose integrating a threshold-locking method into neuromorphic vision sensors to generate sparse and binary information, achieving microsecond-level accurate perception similar to human spiking neurons. By introducing novel Binary Dual Adaptive Training (BAT) and Optically Parallel Mixture of Experts (OPMoE) inference methods, the high-speed, spike-based diffractive optical neural network (S2NN) demonstrates an ultra-fast operating speed of 3649 FPS, which is 30 fold faster than that of reported DONNs, delivering a remarkable computational speed of 417.96 TOPS and a system energy efficiency of 12.6 TOPS/W. Our work demonstrates the potential of incorporating neuromorphic architecture to facilitate optical neural network applications in real-world scenarios for both low-level and high-level machine vision tasks.

physics.optics↗

arXiv2024

Reuse and Blend: Energy-Efficient Optical Neural Network Enabled by Weight Sharing

Optical neural networks (ONN) based on micro-ring resonators (MRR) have emerged as a promising alternative to significantly accelerating the massive matrix-vector multiplication (MVM) operations in artificial intelligence (AI) applications. However, the limited scale of MRR arrays presents a challenge for AI acceleration. The disparity between the small MRR arrays and the large weight matrices in AI necessitates extensive MRR writings, including reprogramming and calibration, resulting in considerable latency and energy overheads. To address this problem, we propose a novel design methodology to lessen the need for frequent weight reloading. Specifically, we propose a reuse and blend (R&B) architecture to support efficient layer-wise and block-wise weight sharing, which allows weights to be reused several times between layers/blocks. Experimental results demonstrate the R&B system can maintain comparable accuracy with 69% energy savings and 57% latency improvement. These results highlight the promise of the R&B to enable the efficient deployment of advanced deep learning models on photonic accelerators.

cs.AR↗

arXiv2023

Highly-efficient fiber to Si-waveguide free-form coupler for foundry-scale silicon photonics

As silicon photonics transitions from research to commercial deployment, packaging solutions that efficiently couple light into highly-compact and functional sub-micron silicon waveguides are imperative but remain challenging. The 220 nm silicon-on-insulator (SOI) platform, poised to enable large-scale integration, is the most widely adopted by foundries, resulting in established fabrication processes and extensive photonic component libraries. The development of a highly-efficient, scalable and broadband coupling scheme for this platform is therefore of paramount importance. Leveraging two-photon polymerization (TPP) and a deterministic free-form micro-optics design methodology based on the Fermat's principle, this work demonstrates an ultra-efficient and broadband 3-D coupler interface between standard SMF-28 single-mode fibers and silicon waveguides on the 220 nm SOI platform. The coupler achieves a low coupling loss of 0.8 dB for fundamental TE mode, along with 1-dB bandwidth exceeding 180 nm. The broadband operation enables diverse bandwidth-driven applications ranging from communications to spectroscopy. Furthermore, the 3-D free-form coupler also enables large tolerance to fiber misalignments and manufacturing variability, thereby relaxing packaging requirements towards cost reduction capitalizing on standard electronic packaging process flows.

physics.optics↗

arXiv2021

Free-form micro-optics enabling ultra-broadband low-loss fiber-to-chip coupling

Efficient fiber-to-chip coupling has been a major hurdle to cost-effective packaging and scalable interconnections of photonic integrated circuits. Conventional photonic packaging methods relying on edge or grating coupling are constrained by high insertion losses, limited bandwidth density, narrow band operation, and sensitivity to misalignment. Here we present a new fiber-to-chip coupling scheme based on free-form reflective micro-optics. A design approach which simplifies the high-dimensional free-form optimization problem to as few as two full-wave simulations is implemented to empower computationally efficient design of high-performance free-form reflectors while capitalizing on the expanded geometric degrees of freedom. We demonstrated fiber array coupling to waveguides taped out through a standard foundry shuttle run and backend integrated with 3-D printed micro-optics. A low coupling loss down to 0.5 dB was experimentally measured at 1550 nm wavelength with a record 1-dB bandwidth of 300 nm spanning O to U bands. The coupling scheme further affords large alignment tolerance, high bandwidth density and solder reflow compatibility, qualifying it as a promising optical packaging solution for applications such as wavelength division multiplexing communications, broadband spectroscopic sensing, and nonlinear optical signal processing.

physics.optics↗