Optical fiber-coupled Kretschmann SPR sensor with re-attachable gold nano-thin film sensing chip.

We designed and demonstrated a portable and reusable surface plasmon resonance (SPR) sensor based on an optical fiber-coupled Kretschmann configuration with a variable detection limit enabled by the re-attachable gold nano-thin film. The prism angle of SPR has been optimized to 63.5 degrees to enable the SPR sensor to operate in the near-infrared band.

Coupling strategy between high-index and mid-index micro-metric waveguides for O-band applications.

The integration of fast and power efficient electro-absorption modulators on silicon is of utmost importance for a wide range of applications. To date, Franz-Keldysh modulators formed of bulk Ge or GeSi have been widely adopted due to the simplicity of integration required by the modulation scheme. Nevertheless, to obtain operation for a wider range of wavelengths (O to C band) a thick stack of Ge/GeSi layers forming quantum wells is required, leading to a dramatic increase in the complexity linked to sub-micron waveguide coupling.

Noise-resilient and high-speed deep learning with coherent silicon photonics.

The explosive growth of deep learning applications has triggered a new era in computing hardware, targeting the efficient deployment of multiply-and-accumulate operations. In this realm, integrated photonics have come to the foreground as a promising energy efficient deep learning technology platform for enabling ultra-high compute rates. However, despite integrated photonic neural network layouts have already penetrated successfully the deep learning era, their compute rate and noise-related characteristics are still far beyond their promise for high-speed photonic engines.

A Review of Capabilities and Scope for Hybrid Integration Offered by Silicon-Nitride-Based Photonic Integrated Circuits.

In this review we present some of the recent advances in the field of silicon nitride photonic integrated circuits. The review focuses on the material deposition techniques currently available, illustrating the capabilities of each technique. The review then expands on the functionalisation of the platform to achieve nonlinear processing, optical modulation, nonvolatile optical memories and integration with III-V materials to obtain lasing or gain capabilities.

Channel response-aware photonic neural network accelerators for high-speed inference through bandwidth-limited optics.

Photonic neural network accelerators (PNNAs) have been lately brought into the spotlight as a new class of custom hardware that can leverage the maturity of photonic integration towards addressing the low-energy and computational power requirements of deep learning (DL) workloads. Transferring, however, the high-speed credentials of photonic circuitry into analogue neuromorphic computing necessitates a new set of DL training methods aligned along certain analogue photonic hardware characteristics. Herein, we present a novel channel response-aware (CRA) DL architecture that can address the implementation challenges of high-speed compute rates on bandwidth-limited photonic devices by incorporating their frequency response into the training procedure.