Building blocks for fast and scalable photonic computing

The current state-of-the-art computing architecture is based on von-Neumann architecture, which involves shuttling off data between memory and the processing unit to carry out computation through the bus. Machine learning algorithms, on the other hand, require massive parallelisation and high data throughput, making von-Neumann's architecture highly inefficient. Therefore, there is a need for specialised hardware to meet the computational demands. Photonics has been proposed to be a promising platform for computing models which are highly distributed and parallel due to the inherent benefits of high interconnectivity and parallelism offered by multiplexing techniques such as wavelength division multiplexing (WDM) and mode division multiplexing (MDM).
So far, various photonic computing architectures have been demonstrated. However, current photonic systems are limited by slow memory cells without write and readout characteristics and efficient arrangement of such memory cells into a network for scalability. Another challenge is related to identifying the right photonic platform with both excellent optical properties and also allowing for the co-integration of electronic and photonic components to build a complete photonic computing system. To overcome these challenges, this thesis investigates functional materials to achieve ultrafast photonic memory operation. Next, a novel architecture is realised, which allows scalability of photonic networks. Finally a promising photonic platform based on silicon carbide for functional material integration to achieve memory device is investigated.
The first part of the thesis is dedicated to the development of an ultrafast integrated photonics memory device using single-element antimony (Sb). Using femtosecond pulses and modulating the number of pulses and their energies, a photonic device based on Sb can be reversibly switched between amorphous and crystalline phases. Each phase demonstrates different optical absorption, therefore, resulting in an optical memory with switching speeds of 500 MHz. Varying pulse energies can also result in up to 7 different memory levels with the potential application of optical analogue computing. Further, the Sb memory cell is shown to possess tuneable volatility, which has been exploited for building an all-optical tunable linear filter.
The next part of the thesis is dedicated to building a scalable photonic network using a reduced rank technique. This technique reduces the redundancy in neural networks and hence decrease the complexity of the entire network. This results in less aggressive scaling of the associated photonic circuitry and offers advantages in terms of a smaller footprint and a lower energy budget in the case of a scaled-up network. A large matrix with integrated photodetectors and modulators is designed using a process design kit (PDK) from a foundry. Next, to further improve a photonic network's scalability and fabrication tolerance, an asymmetrical multimode Y-coupler design has been investigated using simulations. It is estimated that such a Y-coupler design has about a 10 per cent improvement in terms of coupling losses in the network and is tolerant to fabrication variations, therefore improving the reliability of the network.
Finally, a promising photonic platform based on amorphous silicon carbide (SiC) on an insulator is investigated. An integrated photonic platform is achieved with impressive linear and nonlinear properties. Ring resonators with quality factors approaching 0.4 Million are fabricated, corresponding to less than 1 dB/cm waveguide loss, demonstrating excellent linear optical properties. To exhibit third-order nonlinearity, degenerate four-wave mixing experiments are conducted. A conversion efficiency of -23 dB is obtained, showing excellent nonlinear properties of the platform. Finally, a memory cell device based on this SiC is realised. Dynamic switching characteristics of the platform are studied and compared with silicon and silicon nitride photonic platforms.

Advisor: Bhaskaran, H