Switchable metasurfaces from phase change materials designed with machine learning

Chalcogenide phase change materials ( PCMs) are uniquely suited for spectral tuning applications due to their contrasting dielectric material properties. Recent headway has been made towards realizing tunable photonic devices using twodimensional, sub-wavelength resonators by carefully designing geometries that optimize optical, electrical, and thermal performances using multi-physics analyses and machine learning. In this paper, we tackle two other essential aspects for creating application-specific, tunable PCM devices: (1) scalability of the device size and (2) high-throughput fabrication techniques. We employ a deep ultraviolet (DUV) stepper projection lithography to manufacture over 100 densely packed GST metasurfaces, each with a sample size of 5x7 mm(2), all on a 4-inch Al2O3 wafer. These metasurface structures were discovered using artificial neural network (ANN) techniques and confirmed by finite-difference- time domain calculations. The primary structures under investigation were nanobar configurations enabling amplitude modulation at short-wave infrared wavelengths to realize efficient optical switches for free space optical multiplexing. The DUV fabrication technique can easily be extended to other metasurface geometries to demonstrate multi-functional, non-volatile photonic devices.