Artificial intelligence has a strong need for faster and more energy-efficient solutions, especially for computation performed at the sensor edge. On-chip photonic neural networks (PNNs) offer a promising solution for high speeds and energy efficiency. A less explored side of PNNs is their application to time-series data, which is often the case for real-world sensor applications. While PNNs promise high speeds and energy-efficient solutions, no good use cases have been proposed. This report will first review the state of the art of PNNs. It will be seen that to solve time-series tasks, photonic continuous-time recurrent neural networks (CTRNNs) are required. The dynamics of CTRNNs are thoroughly explored to leverage obtained insights to recommend novel practical applications. This is done through simple examples, and applied to a real machine learning task. It was seen that on a real classification task the network learned two distinct fixed points corresponding to the classes. A link between the time constant of the continuous-time neurons and the temporal dynamics of the task is also found. Two general directions for novel applications are then proposed. Firstly, photonic PNNs can be slowed down to match the task. Opto-electronic PNNs allow for more control of the time constant, and on-chip photonic filter neurons are suggested. Secondly, the high speeds of photonic neural networks can also be directly leveraged. Extremely fast convergence of photonic CTRNNs can be utilized for Modern Hopfield networks, pathfinding algorithms, and time-dependent optimization problems such as for example Model Predictive Control.

The current frontier of human exploration is space. We have achieved sending rovers to planets and probes throughout the Solar System and beyond into interstellar space, yet the stars seemed to be out of reach. The current fastest man-made object would take thousands of years to travel to the nearest star, Proxima Centauri. However, Breakthrough Starshot presents an engineering challenge to reach it in a generation's time. Recent technological advancements make this possible through ultralight spacecraft equipped with mirror-like sails, known as Lightsails. Due to their structure, these sails could be propelled by lasers up to 20% light speed, or over 200 million km/h. To achieve the required efficiency, the sails need to exhibit high reflectivity and low mass on the scale of 1g, while covering a surface area of ∼10m2. Some materials and membranes explored in literature have exhibited high reflectivity, but their mass did not comply with the necessary requirements. The most promising current design is a photonic crystal, which interacts with light in a way that maximizes reflectivity and removes material by incorporating cavities into the surface, effectively reducing overall mass. Current research into photonic crystal Lightsails primarily focuses on the reflectivity of a small segment of a flat lattice. While this is essential, the literature suggests that the full-scale structures will probably behave like traditional sails on sailboats, tending to billow. This prompts us to consider not only the problem of reflectivity on curved surfaces but also mechanical deformations and stresses within a membrane that's 1000 times thinner than a human hair. This thesis investigates the full-scale design of the Lightsail from both the structural and electromagnetic sides. Firstly, the material, microstructure, and macrostructure of the Lightsail are analyzed to determine stresses and deformations, as well as the optimal shape for stress distribution and efficiency. Then, through topology optimization of shell elements, the main load-carrying "backbone" of the sail is identified. Secondly, optimization of unit cell photonic crystal cavities is performed across the curvature of the sail derived from the previously obtained shape, maximizing the reflectivity. Finally, the thesis proposes a design of the Lightsail that integrates all findings, which is then used to obtain a Figure of Merit value for comparison with designs in existing literature.