Integrated phase-change in-memory photonic computing

In highly-developed societies, easy access to a vast resource of information has gradually become a necessity instead of a choice. People are not only exchanging more information for entertainment, but also relying increasingly heavily on large amounts of data to make decisions. For instance, artificial intelligence relies on an extensive database to have a more precise predictive ability, which is widely used nowadays, such as access control systems using facial recognition, gait recognition or even iris scanning. In the process of diagnosing major diseases, instantaneous access to enormous medical records helps generate a more effective and potent treatment solution quicker. All aspects of people's lives, not just security and health, bring a high demand for a great deal of data processing. In many respects, the performance of data processing is closely related to the quality of people's life.
However, the efficiency of current computing and storage systems is far from adequate to satisfy the abovementioned data processing demands. Moore's law is close to reaching its physical limit, the performance gap between CPU and memory is difficult to bridge, and there is as yet no solution that can simultaneously satisfy the problems of energy consumption and computing performance. People still cannot but suffer when shopping websites crash on Black Fridays.
Fortunately, integrated photonic in-memory computing provides a potential solution to help with the current data processing situation. Photonic processing has high speed and immense throughput volume because of wavelength multiplexing's capability for parallel processing with hundreds or even thousands of channels. Furthermore, the addition of phase-change material to the integrated photonic system makes it possible to process and store data in the same position, eliminating data transport energy consumption. Together with well-established silicon photonics foundry resources and widespread optical fiber transmission systems, the influence of photonic computing and storage systems will be profound and significant once developed.
This thesis details several milestones on the path towards achieving an integrated phase-change photonic computing system. The research processed through stages covering single device modulation optimization, substrate material investigation, and employing single-device solutions to scale up to a 3 × 3 matrix demonstration system. Every section includes optical or thermal modelling, material calibration, device fabrication and optical measurements.
The first project demonstrated an optically controlled photonic multi-bit memory device with 34 different data storage levels (over 5 bits) in a single memory cell, providing an order of magnitude improvement over previous demonstrations in terms of time and energy consumption. For this stage, numerous methods of optical control and mechanisms behind optical switching are also discussed. Following this up, double-step single-shot modulation was demonstrated to achieve arbitrary data storage level addressing and retrieval without information of former statuses. This work has shown the potential for high-density, low energy cost, and all-optical storage performance through a single device. It pushed the limit of the performance of single devices to their practical maximum by optimizing the controlling method and measuring environment, a milestone for non-volatile integrated photonic memory and computing systems.
Platform investigation was performed after device optimization for a broader range of applications and more practical usage. The phase-change photonic computing devices were transferred from a silicon nitride platform to a silicon platform. A detailed comparison was carried out between the two platforms addressing substrate material, device footprint, switching contrast, energy consumption and switching speed. This substrate investigation follows the path towards, upon the optimization of single devices, achieving a high speed, low energy cost optical computing and storage system.
Finally, a small phase-change optically controlled matrix was demonstrated with 3 × 3 photonic in-memory computing cells that are connected. This matrix was developed for its ability to serve as a matrix factor to realize easy-programmable, non-volatile in-memory matrix-vector multiplication operations. Its constituent, crucial components were calibrated beforehand, including the single phase-change computing cell's switching contrast and speed, as well as directional couplers and thermal phase shifters to modulate passive splitting ratio and active splitting ratio between different branches, respectively. Moreover, an electrically modulated Mach-Zender interferometer array was added to the photonic matrix to achieve a denser footprint and an automatic weight setting. Two applications were carried out to demonstrate this matrix's functionality: small pattern recognition based on a single perception neural network; and three-dimensional images rotation. This phase-change photonic matrix multiplication core enabled large-scale on-chip non-volatile photonic in-memory computing and demonstrated a capability for solving real problems and tasks in a high speed, low energy consumption manner. This demonstration provides a potential solution to the conflict between an already enormous and still growing data processing burden and the currently overwhelmed available computing and storage systems.

Keywords: in-memory computing
optical computing
phase change material
integrated photonics
Advisor: Bhaskaran, H