Nanophotonic-enhanced solid-state quantum emitters with AI-optimized photon extraction efficiency

This study introduces a powerful AI-based inverse design approach for greatly improving the efficiency of photon extraction from nitrogen-vacancy (NV) centers in diamond. We circumvent the challenges associated with conventional symmetric designs by employing a genetic algorithm to enhance intricate, free-form nanophotonic devices. Full-wave simulations indicate that the Purcell enhancement factor is 18.7, representing a 3.2-fold improvement over conventional nanopillars. Experimental validation confirms an average Purcell factor of 17.9 and a single-photon brightness of about 4 million counts per second, signifying a threefold improvement in performance. The produced devices exhibit non-intuitive, fractal topologies that effectively minimize total internal reflection and promote direct emission into free-space modes. This study demonstrates the potential of computational inverse design to improve solid-state quantum light sources for scalable quantum technology.

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