Expedited circular dichroism prediction and engineering in two-dimensional diffractive chiral metamaterials leveraging a powerful model-agnostic data enhancement algorithm

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.

Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions

“…Furthermore, data enhancement based on this model has shown a high reliability. Since the absolute values of reflection spectra usually vary between 0 and 1, it is necessary to convert the spectra into a space with greater absolute values in order to make the network extraction much easier [ 34 ]: where y represents the original spectra and y′ denotes the values after calculation. The loss function of the forward prediction network is described by the mean absolute error (MAE) [ 37 ]: where n is the batch size, y’ pred represents the prediction of the spectra, and y’ true is the corresponding true reflection spectra.…”

“…The DL algorithms are employed in various fields, such as nature language processing [ 24 , 25 , 26 ], image recognition [ 27 ], finance [ 28 , 29 , 30 ], and medicine [ 31 , 32 , 33 ]. Especially for the aspect of nanophotonics, the DL approach has proved to be one of the most advanced tools to study many nonintuitive and nonlinear physics issues [ 34 , 35 , 36 , 37 ], including the problem of the inverse design of photonics devices [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Peurifpy et al [ 46 ] utilized 50,000 samples to train neural networks to design a multi-layer dielectric spherical nanoparticle in 2018, which was regarded as the landmark in this field.…”

Deep learning for circular dichroism of nanohole arrays

“…DL is being increasingly utilized in diverse fields, such as fiber optics [24], semiconductors [25], and design of electromagnets [26,27]. Researchers have already used deep learning to implement the study of chiral nanostructures [28][29][30]. In nanophotonics, DL has been adopted for resonant mode analysis [31,32] and spectra calculation [33,34].…”

Machine learning assisted GaAsN circular polarimeter

“…These techniques have been applied to the solution of a wide range of problems in optoelectronics as the identification of light sources [70], the detection and classification of defects in transparent substrates [71], the calibration of single-photon detectors [72], the characterization and design of photonic crystals [73][74][75], and the detection enhancement of quadrant photodiodes [76]. More specifically, machine learning has also proven to be quite useful in the context of circular polarization detection in improving the performance and accuracy of a Stokes polarimeters [77], analyzing the nearfield intensity distribution to determine the arbitrary states of polarization in a circular polarimeter based on plasmonic spirals [78] and in investigating the circular dichroism properties in the higher-order diffracted patterns of two-dimensional chiral metamaterials [79].…”