Multifunctional Meta-Optic Systems: Inversely Designed with Artificial Intelligence
This work addresses the problem of designing compact, multifunctional optical devices for applications in optics and photonics, representing a novel method rather than an incremental improvement.
The authors tackled the challenge of designing multifunctional meta-optic systems, which are hindered by traditional methods' computational complexity and limitations in controlling polarization, frequency, and spatial channels independently. They presented an AI framework that demonstrated examples like a polarization-multiplexed dual-functional beam generator, achieving capabilities barely attainable by single-layer metasurfaces or traditional processes.
Flat optics foresees a new era of ultra-compact optical devices, where metasurfaces serve as the foundation. Conventional designs of metasurfaces start with a certain structure as the prototype, followed by an extensive parametric sweep to accommodate the requirements of phase and amplitude of the emerging light. Regardless of how computation-consuming the process is, a predefined structure can hardly realize the independent control over the polarization, frequency, and spatial channels, which hinders the potential of metasurfaces to be multifunctional. Besides, achieving complicated and multiple functions calls for designing a meta-optic system with multiple cascading layers of metasurfaces, which introduces super exponential complexity. In this work we present an artificial intelligence framework for designing multilayer meta-optic systems with multifunctional capabilities. We demonstrate examples of a polarization-multiplexed dual-functional beam generator, a second order differentiator for all-optical computation, and a space-polarization-wavelength multiplexed hologram. These examples are barely achievable by single-layer metasurfaces and unattainable by traditional design processes.