Design and Optimisation of Optical Metasurfaces Using Deep Learning

This thesis centres on the design, processing, and fabrication of tunable optical metamaterials. It incorporates physics-based simulation, deep learning (DL), and thin film fabrication techniques to offer a comprehensive exploration of the field of optical metamaterials. Placing stiff resonators on a flexible substrate is a common type of mechanically tunable metasurface, whose optical responses are tuned by dynamically adjusting the spacing between resonators by applying mechanical force. However, the significant modulus mismatch between materials causes stress concentration at the interface, leading to crack propagation and delamination at lower strain levels (20-50%), and limiting the optical tunability of the structure. To address this challenge, we propose two designs to manipulate stress distribution. Under mechanical force, the structure enables localised deformation, redirecting stress from critical areas. This mechanism minimises the accumulation of stress in the interface, thereby diminishing the risk of material failure and improving stretchability up to 120% compared to traditional designs. This extreme stretchability leads to a 143 nm resonance shift, which is almost twice as large as that of conventional geometry. A universal machine learning (ML)-based approach was developed to optimise the metasurface design across three key aspects: geometric parameters, material development, and free-form shape configuration. In design parameters optimisation, a fully connected neural network (FCNN) was developed with a mean absolute error (MAE) of 0.0051, recommending a single geometry with a 104 order of magnitude decrease in computational time when compared to finite element method (FEM) simulations used for data generation. The suggested structure provides extensive coverage of the colour space, encompassing 27.65% of the standard RGB (sRGB) space. For the materials development part, an inverse design (ID) network was combined with effective medium approximation (EMA), navigating infinite materials composition space to identify new compositions for custom applications. The last network was tasked to explore boundless free-form shape space to propose the one for the on-demand optical properties with MAE of 0.21. The accuracy of all networks was experimentally validated

Acoustic Waves

The concept of acoustic wave is a pervasive one, which emerges in any type of medium, from solids to plasmas, at length and time scales ranging from sub-micrometric layers in microdevices to seismic waves in the Sun's interior. This book presents several aspects of the active research ongoing in this field. Theoretical efforts are leading to a deeper understanding of phenomena, also in complicated environments like the solar surface boundary. Acoustic waves are a flexible probe to investigate the properties of very different systems, from thin inorganic layers to ripening cheese to biological systems. Acoustic waves are also a tool to manipulate matter, from the delicate evaporation of biomolecules to be analysed, to the phase transitions induced by intense shock waves. And a whole class of widespread microdevices, including filters and sensors, is based on the behaviour of acoustic waves propagating in thin layers. The search for better performances is driving to new materials for these devices, and to more refined tools for their analysis

Optical wireless data transfer for rotor detection and diagnostics

A special application of optical wireless data transfer, namely on-line monitoring and diagnostic of rotors in turbines and engines, has been considered in this thesis. In this application, to maintain line of sight, i.e. data transfer, between a sensor placed on a rotating component inside the turbine and a monitoring point placed in a fixed position outside the turbine, a periodic fast fading channel is generated, which gives the transceivers more flexibility regarding their mounting location. The communication in such a channel is affected by the intermittency and variation of the signal power, which produces a unique channel condition that influences the performance of the optical transceiver. To investigate the channel condition and the error rate of the periodic fast fading channel with signal fluctuation, a model is developed to simulate the optical channel by considering the variation of signal power as a result of the change in the relative position of the photodiode with respect to the Lambertian radiation pattern of the LED, in a simplified linear geometry. The error rate is estimated using the Saddlepoint approximation on a specific threshold strategy. The results show that the channel can afford the sensor data transmission and the performance can be improved by modifying several parameters, such as geometrical distance, transmitter power and load resistor. Compared to a normal channel, a higher load resistor on the photodiode front end has the advantage of decreasing the noise level and increasing the data capacity in the fast fading channel. The analysis of the automatic gain control amplifier indicates that a higher load resistor needs a lower loop gain and from the model of the Transimpedance amplifier (TIA), the bandwidth extension from the amplifier is more significant for a higher resistor. In addition to the theoretical model, an experimental setup is built to emulate the channel in practice. The degree of similarity between the experimental setup and the theoretical model of the channel is estimated from the comparison of the generated communication windows. Since it has been found that differences exist in the duration of the communication window and the variation of the signal power, scaling factors to ensure their compatibility have been derived. Transceiver hardware which implemented the modelled functionality has been developed and a protocol to establish the communication with the required error rate has been proposed. Using the hardware implementation, a detection method for both rising and falling edges of the signal pulses and a threshold strategy have been demonstrated. The device power consumption is also estimated. What is more, the electromagnetic environment of a squirrel cage motor is simulated using the finite element method to investigate the interference and the possibility of providing power to the IR communication devices using power scavenging. In the conclusion, the key findings of the thesis are summarised. A solution is proposed for sensor data transfer using an optical channel for rotor monitoring applications, which involves the design of the IR transceiver, the implementation of the developed protocol and the power consumption estimation

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data