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Optimizing the shapes and topology of physical devices is crucial for both scientific and technological advancements, given their wide-ranging implications across numerous industries and research areas. Innovations in shape and topology optimization have been observed across a wide range of fields, notably structural mechanics, fluid mechanics, and more recently, photonics. Gradient-based inverse design techniques have been particularly successful for photonic and optical problems, resulting in integrated, miniaturized hardware that has set new standards in device performance. To calculate the gradients, there are typically two approaches: namely, either by implementing specialized solvers using automatic differentiation (AD) or by deriving analytical solutions for gradient calculation and adjoint sources by hand. In this work, we propose a middle ground and present a hybrid approach that leverages and enables the benefits of AD for handling gradient derivation while using existing, proven but black-box photonic solvers for numerical solutions. Utilizing the adjoint method, we make existing numerical solvers differentiable and seamlessly integrate them into an AD framework. Further, this enables users to integrate the optimization environment seamlessly with other autodifferentiable components such as machine learning, geometry generation, or intricate post-processing which could lead to better photonic design workflows. We illustrate the approach through two distinct photonic optimization problems: optimizing the Purcell factor of a magnetic dipole in the vicinity of an optical nanocavity and enhancing the light extraction efficiency of a µ LED.

We exploit InSb’s magnetic-induced optical properties to design THz sub-wavelength antennas that actively tune the radiative decay rates of dipole emitters at their proximity. The proposed designs include a spherical InSb antenna and a cylindrical Si-InSb hybrid antenna demonstrating distinct behaviors. The former dramatically enhances both radiative and non-radiative decay rates in the epsilon-near-zero region due to the dominant contribution of the Zeeman-splitting electric octupole mode. The latter realizes significant radiative decay rate enhancement via magnetic octupole mode, mitigating the quenching process and accelerating the photon production rate. A deep-learning-based optimization of emitter positioning further enhances the quantum efficiency of the proposed hybrid system. These novel mechanisms are promising for tunable THz single-photon sources in integrated quantum networks.