KEYWORDS: Sum frequency generation, Plasmonics, Deep learning, Upconversion, Nonlinear metamaterials, Photons, Second harmonic generation, Nonlinear optics
Photonic upconversion from the infrared regime to the visible spectrum can occur through sum-frequency generation (SFG). A second-order nonlinear optical response, such as SFG, can be produced from a nonlinear material, in this case an ABC nanolaminate. Optimization of a metamaterial consisting of a plasmonic nanolaminate device can maximize the SFG from incident wavelengths. Utilization of a deep learning framework removes the need for traditional guess and check methods and creates new possibilities for plasmonic geometries. Applications of this research include low-cost night vision or low light imaging systems for defense, autonomous vehicles, and other commercial uses.
Traditional processes for the design of metamaterial structures are often computational heavy, time-consuming, and occasionally does not lead to the desired optical response. Deep learning can quickly optimize structures through inverse design, and create new geometries for devices. This research uses a deep learning framework for the inverse design of an optimal plasmonic structure to maximize the second-order nonlinear response from a nonlinear metamaterial. The thinfilm nonlinear metamaterial employed is a nanolaminate, and the optimal plasmonic structure is fabricated to establish the validity of the deep learning algorithm.
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