Monday, September 30, 2024 – 10:00 am Zoom
Dissertation title: “Imaginary-Index-Driven Programmable Integrated Photonics for Optical
Computing and Networking”
Abstract: Photonics serves as the backbone of modern information infrastructure, transmitting
and processing data at unparalleled speeds with minimal energy consumption by harnessing the
inherent parallelism, high-frequency operation, and expansive bandwidths. In the past decade,
the surging advancements of artificial intelligence has revolutionized the traditional definition of
computing algorithms. By bridging the gap between optical hardware and software-defined
functionality, programmable integrated photonics, where on-chip photonic circuits are
dynamically reconfigured by tunable optical components including modulators, amplifiers, and
switches, opens new avenues for optical routing, computing and networking. However, the
existing programmable integrated photonic platforms employ discrete, single-function devices,
leading to exponential architectural complexity and hindering full programmability. Additionally,
fabrication imperfections may compromise performance, impeding the advancement of large-
scale photonic processors designed for data-intensive applications. In contrast to the state of the
art, we explores programmable integrated photonic platforms driven by the imaginary part of the
permittivity in semiconductor-based optical gain materials. First, a topological photonic system
is presented, demonstrating robust and reconfigurable light steering immune to fabrication
defects, driven by non-Hermitian physics. By interacting with the pseudospin degree of freedom,
defined by the circulating direction in photonic cavities, a non-blocking scheme is realized. Next,
a novel lithography-free paradigm for integrated photonic computing is proposed and
demonstrated in an unpatterned device fully driven by the imaginary index. This new platform
enables field-programmability and dynamic robustness, culminating in a high-fidelity photonic
matrix processor capable of real-time error correction and in-situ photonic network training for
practical tasks. Furthermore, the capabilities of photonic field-programmability can be pushed
into the nonlinear realm by the spatial control of carrier excitations and their dynamics within the
active semiconductor, achieving programmable photonic nonlinear functions. Leveraging the
architecture of photonic nonlinear computing through polynomial building blocks, training of
integrated photonic polynomial networks is demonstrated. This new type of neural networks
serves as a pioneering example in the exploration of photonic paradigms tailored for computing
and networking with light.