General Inverse Design of Thin-Film Metamaterials With Convolutional Neural Networks

Abstract

The design of metamaterials which support unique optical responses is the basis for most thin-film nanophotonics applications. In practice this inverse design problem can be difficult to solve systematically due to the large design parameter space associated with general multi-layered systems. We apply convolutional neural networks, a subset of deep machine learning, as a tool to solve this inverse design problem for metamaterials composed of stacks of thin films. We demonstrate the remarkable ability of neural networks to probe the large global design space (up to 10 12 possible parameter combinations) and resolve all relationships between metamaterial structure and corresponding ellipsometric and reflectance / transmittance spectra. The applicability of the approach is further expanded to include the inverse design of synthetic engineered spectra in general design scenarios. Furthermore, this approach is compared

Deep learning modeling strategy for material science: from natural materials to metamaterials

“…Finally, local crystallography techniques can be used to further study the selected defects. In metamaterials, CNN can be used to predict the optimal metamaterial design [81], and solve the inverse design problem between metamaterial structure and response [31]. As shown in figure 3(c), the developed model solves the mapping relationship between the metamaterial structure and the corresponding ellipse measure and reflectance/transmittance spectrum, highlighting the remarkable ability of neural networks in detecting large global design spaces.…”

“…Target application References Model functionality MLP Natural material [33,61,62] Provide acceleration and approximate simulation for traditional computational physics and inverse design [65][66][67][68][69][70] Predict material properties, such as formation energy, band gap, melting point, and promote the discovery of new materials Metamaterial [44,71,72,[74][75][76] Predict the relationship between the structural parameters of metamaterials and the electromagnetic response; realize on-demand inverse design [34,45,73] Optimize the design method, reduce the amount of calculation, and make the automatic design possible CNN Natural material [47,59,78,79] Simulate the periodic structure, accurately and quickly predict its performance based on the material Metamaterial [31,48,80] Solve the inverse design problem between structure and response [81][82][83] Advanced models promote the development of metamaterial intelligent design GNN Natural material [52,53,89] predict the historically related responses of materials [90][91][92] Accelerate the multi-scale simulation of composite materials Metamaterial [93] Predict the relationship between spatial information and absorption spectra RNN Natural material [48, 57, 58, 100-102, 104, 105] Take into account the interaction between particles in the material, so as to flexibly predict material properties. Improve the interpretability of the model…”

“…In material science, it is used to predict and evaluate the fundamental properties of materials, such as wave functions [21,22], formation energy [23,24], and electronic ground and excited states [25], read in the chemical literature [26,27], crystal structure prediction [28] and micro in the task of structural classification [29]. In metamaterial design tasks, DL can solve complex metamaterial design, help discover the complex and non-intuitive relationship between metamaterial structure and its optical response [30], realize on-demand inverse design [31], performance prediction [32], and improve design speed [33,34] etc.…”

Inverse Design of Distributed Bragg Reflectors Using Deep Learning

“…Removal of non-unique instances followed by DL Lininger et al implement a feedforward neural network to design a 1-5-layer system using up to 5 different materials, with a user-imposed 'similarity metric' [14]. This is used to filter out training instances deemed as non-unique, effectively reducing the design challenge to a one-to-one problem.…”

“…For these reasons, Machine Learning (ML) has moved to the forefront of pioneering inverse design methods [13]. ML employs a range of algorithms to analyse large amounts of data and map complex relationships between information, in order to make future predictions about new data [11,14]. Although the input data are still produced through simulations, this computational cost occurs only once and the burden can be spread across several devices in parallel [15].…”