With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that have been highly successful in applications such as image classification and speech processing. We present an architecture to implement a photonic CNN using the Fourier transform property of integrated star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset. We also model component imperfections in photonic CNN and show that the performance degradation can be recovered in a programmable chip. Our proposed architecture provides a large reduction in physical footprint compared to current implementations as it utilizes the natural advantages of optics and hence offers a scalable pathway towards integrated photonic deep learning processors.

There is an urgent need for industrial Internet of things (IoT) solutions to deploy a smart hydrophone sensor grid to monitor pipeline health and to provide an accurate prediction in the event of any leakage. One solution is to develop an IoT water leakage detection system consisting of an interface to capture acoustic signals from aluminum nitride (AlN)-based micro-machined infrasonic hydrophone sensors that are fed as inputs and predict an approximate leak location as a form of output. Micro-electro-mechanical systems (MEMS) are particularly useful for IoT applications with low power consumption and small device footprint. Data analytics including characterization, pre/post processing are applied to determine the leaks. In this work, we have developed the process flow and algorithm to detect pipe leakage occurrence and pinpoint the location accurately. Our approach can be implemented to detect leaks for different pipe lengths, diameters and materials.

We developed a way of predicting and analyzing high speed optical modulator. Our research adopted a bottom-up approach to consider high-speed optical links using an eye diagram. Our method leverages on modular “mapping” of electrical characteristics to optical characteristics, while attaining the required accuracy necessary for device footprint approaching sub-micron scales where electrical data distribution varies drastically. We calculate for the bias dependent phase shift (2pi/mm) and loss (dB/mm) for the optical modulator based on the real and imaginary part of complex effective indices. Subsequently, combine effectively both the electrical and optical profiles to construct the optical eye diagram which is the essential gist of signal integrity of such devices.

Submicron optical waveguides exhibit strong dependency on its polarisation states, making polarisation insensitive design a challenge. We study both photonic wires and rib waveguides that are possible to satisfy both singlemode and zero birefringence simultaneously.