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We present the machine learning inspired design of two types of GaN zero contrast subwavelength gratings (SWGs): a polynomial-shaped grating and, for reference, a conventional rectangular grating, using the differential evolution algorithm to optimize the designs for high broadband reflectivity and separately for large fabrication tolerance. Two wavelength regimes with 500 nm and 1.55 &#x03BC;m center wavelengths (&#x03BB;center) are investigated. Our results indicate that both polynomial and rectangular grating designs can achieve comparable stopband widths of 170 nm (&#x0394;&#x03BB;) for 500 nm (&#x0394;&#x03BB;/&#x03BB;center = 34%). For the 1.55 &#x03BC;m center wavelength, the 482 nm (&#x0394;&#x03BB;/&#x03BB;center = 31%) stopband width of the polynomial grating is slightly less than that of the rectangular grating at 498 nm (&#x0394;&#x03BB;/&#x03BB;center = 32%). We also demonstrate design for enhanced fabrication tolerance while placing a minimum constraint on the stopband width. Our results show the rectangular grating exhibits slightly higher fabrication tolerances for grating parameters than the polynomial-shaped grating in general. This work also outlines the technique we have adopted for the inverse design of these gratings.

Machine Learning Inspired Design of Complex-Shaped GaN Subwavelength Grating Reflectors