Design of a gradient-index beam shaping system via a genetic algorithm optimization method

Geometrical optics - the laws of reflection and refraction, ray tracing, conservation of energy within a bundle of rays, and the condition of constant optical path length - provides a foundation for design of laser beam shaping systems. This paper explores the use of machine learning techniques, concentrating on genetic algorithms, to design laser beam shaping systems using geometrical optics. Specifically, a three-element GRIN laser beam shaping system has been designed to expand and transform a Gaussian input beam profile into one with a uniform irradiance profile. Solution to this problem involves the constrained optimization of a merit function involving a mix of discrete and continuous parameters. The merit function involves terms that measure the deviation of the output beam diameter, divergence, and irradiance from target values. The continuous parameters include the distances between the lens elements, the thickness, and radii of the lens elements. The discrete parameters include the GRIN glass types from a manufacturer's database, the gradient direction of the GRIN elements (positive or negative), and the actual number of lens elements in the system (one to four).

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