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In the emerging field of photonic nano-structures, the optimization of chiral meta-surfaces has emerged as a pivotal challenge, particularly for applications such as asymmetric transmission, circular dichroism (CD) spectroscopy, imaging, and spin-selective absorption. Traditional metasurface design methodologies have often been tied to laborious parameter tuning and iterative simulations, demanding both computational resources and domain expertise. This work introduces a faster approach by leveraging advanced deep-learning algorithms to streamline the optimization of chiral meta-nano surfaces. While using diatomic unit-element as the meta-surface’s building blocks, our proposed methodology harnesses the power of neural networks to predict and refine the geometrical layout of achiral nano-bars. The proposed framework is a Tandem Inverse Model (TIM) that incorporates a forward asymmetric transmission predicting neural network (ATNN) cascaded with an inverse neural network (INN). ATNN is trained in advance to enable swift and accurate prediction of the asymmetric optical behavior of meta-atoms with an MSE as low as 5.8 × 10-4. The complete TIM assembly is then trained together while updating the weights of INN only and keeping the pre-trained ATNN part frozen. This stacked arrangement of the forward and the inverse design models successfully addresses the fundamental non-uniqueness issue suffered in the inverse design problems. With an MSE of about 3, the trained TIM model can optimize the nano-bar’s geometrical characteristics very rapidly. The suggested model, therefore, greatly accelerates the process of designing intricate chiral meta-atoms by simultaneously optimizing eight geometrical parameters in a matter of seconds. With this model, the optimal geometrical parameters of the achiral nano-bars of the meta-atom exhibited an AT of approximately 70%. Realizing such a high AT offers several uses, including lasers, optical cloaking, and electromagnetic shielding.
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