Research

Research themes in integrated photonics

Research is organized by physical and computational theme, with full-wave electromagnetic simulation and device physics as the underlying methodology connecting structural geometry to optical and electrical behavior.

Integrated Nanophotonics

Topic publications

Mode profile / structure

Problem: Harnessing the interaction between guided optical modes, hybrid active materials (ferroelectrics, liquid crystals, 2D layers), and surrounding acoustic, thermal, or electrostatic fields in cavity, photonic crystal, plasmonic, and bound-state-in-the-continuum (BIC) structures.
Methods: Full-wave electromagnetic simulation, anisotropic permittivity tensor modeling, multi-physical finite-element coupling, waveguide dispersion engineering, and resonant cavity analysis.
Physical Relevance: External stimuli and material responses dynamically modify the local permittivity tensor, shifting resonances and tuning propagation constants. Modeling these optoacoustic or electro-optic couplings enables active tunability, reconfigurability, and switchable light routing on-chip.

Quantum Photonics

Mode profile / structure

Problem: Scaling on-chip quantum technologies by co-integrating waveguide-based nonlinear photon-pair sources, coherent interfaces, and phase-shifter networks within the low-loss routing topologies established on the nanophotonic platform.
Methods: Nonlinear optics modeling, quantum state tomography representation, coherent waveguide interface simulation, and reconfigurable circuit design.
Physical Relevance: Governing waveguide nonlinear coefficients and coherent interactions to execute high-fidelity quantum state preparation, routing, and manipulation in photonic circuit topologies.

Intelligent Photonics

Topic publications

Mode profile / structure

Problem: Accelerating device design and discovery by bridging wave equations with deep learning and optimization methods, while developing all-optical neuromorphic processing hardware.
Methods: Physics-informed neural networks (PINNs), adjoint and gradient-free optimization, evolutionary search algorithms, optical neural network (ONN) architectures, and wave-based co-design.
Physical Relevance: Establishing a bidirectional framework where physical wave propagation constrains network training, and multi-port interferometric circuits execute mathematical operations at the speed of light.