Phase-change Programmable Photonics for Optical Computing and Signal Processing

Abstract

The programmability in integrated photonic systems fosters advancements across diverse technologies, from data centers to optical neural networks and quantum information processing. Phase-change materials (PCMs) can offer an ideal solution thanks to their reversible switching, large index contrast, and non-volatile behavior, enabling programmability with no static power consumption. In this thesis, I will mainly introduce several phase-change photonic devices that can contribute to various photonic applications such as optical computing, signal processing, and optical communications.First, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising for large-scale photonic neural networks with ultrahigh computation throughputs. Then we demonstrate a photonic generative network as a part of a generative adversarial network (GAN) that can generate a handwritten number in experiments. We realize an optical random number generator derived from the amplified spontaneous emission noise, apply noise-aware training by injecting additional noise, and demonstrate the network’s resilience to hardware non-idealities. Our results suggest the resilience and potential of more complex photonic generative networks based on large-scale, realistic photonic hardware. Finally, we report direct-write and rewritable photonic circuits based on a low-loss phase change material (PCM) thin film, in which complete end-to-end functional photonic circuits can be created by direct laser writing in one step without additional fabrication processes. The direct-write phase-change photonic circuit affords exceptional flexibility, allowing any part of the circuit to be erased and rewritten, facilitating rapid design modification and reprogramming. We demonstrate the versatility of this technique with various photonic circuits for diverse applications, including an optical interconnect fabric for reconfigurable networking, a photonic crossbar array as a tensor core for optical computing, and a tunable optical filter for optical signal processing.