Abstract
Abstract A model-agnostic data enhancement (MADE) algorithm is proposed to comprehensively investigate the circular dichroism (CD) properties in the higher-order diffracted patterns of two-dimensional (2D) chiral metamaterials possessing different parameters. A remarkable feature of MADE algorithm is that it leverages substantially less data from a target problem and some training data from another already solved topic to generate a domain adaptation dataset, which is then used for model training at no expense of abundant computational resources. Specifically, nine differently shaped 2D chiral metamaterials with different unit period and one special sample containing multiple chiral parameters are both studied utilizing the MADE algorithm where three machine learning models (i.e, artificial neural network, random forest regression, support vector regression) are applied. The conventional rigorous coupled wave analysis approach is adopted to capture CD responses of these metamaterials and then assist the training of MADE, while the additional training data are obtained from our previous work. Significant evaluations regarding optical chirality in 2D metamaterials possessing various shape, unit period, width, bridge length, and separation length are performed in a fast, accurate, and data-friendly manner. The MADE framework introduced in this work is extremely important for the large-scale, efficient design of 2D diffractive metamaterials and more advanced photonic devices.
Highlights
Optical chirality is viewed as one of the most promising and alluring aspects pertaining to chirality, a ubiquitous phenomenon in nature
One crucial format of optical chirality, namely the circular dichroism (CD) [10], which describes the absorption variation when irradiated by the left circularly polarized (LCP) and right circularly polarized (RCP) light, should be acknowledged here
We introduce and employ a data enhancement algorithm associated with different machine learning (ML) methods to predict the optical chirality of various 2D diffractive chiral metamaterials, aiming at reducing the amount of training data with the assistance of a formerly addressed problem, whose key routine is recognized as the model-agnostic data enhancement (MADE) algorithm
Summary
Optical chirality is viewed as one of the most promising and alluring aspects pertaining to chirality, a ubiquitous phenomenon in nature. Deep learning networks or machine learning (ML) methods are significant for a wide range of scientific and industrial processes, covering the aspects of finance [26, 27], medicine [28,29,30], transportation [31,32,33], communication [34, 35], nanoscience [36,37,38,39], and sensors [40] These learning-based algorithms have emerged as one of the most successful research routes for computational physics and photonic device design [41,42,43,44,45,46]. Remarkable and significant superiorities of such algorithm over the traditional numerical methods or pure ML approaches are revealed, in consideration of accuracy, computational speed, and resources, as well as the generalizable and flexible abilities
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