Machine Accelerated Nano-Targeted Inhomogeneous Structures

Abstract

Optical metamaterials can achieve optical performance specifications otherwise unattainable compared to their bulk constituents via the use of skillfully engineered nanoscale features. These features function as sub-wavelength optical scatterers, causing incident light to interact with metamaterials in a manner which is crucially dependent on their geometry. To design the next generation of functional materials, scientists and engineers must realize new methods which prudently explore the near-infinite number of possible metamaterial arrangements. We present a general methodology which harnesses machine learning to accelerate the process of metamaterial design and apply it specifically here, as a test case, to a silicon on insulator (SOI) reflective metasurface consisting of an array of nano-pillars. In particular, we implement artificial neural networks (ANNs) to solve the inverse design problem; i.e. we prescribe a desired reflection profile and utilize ANNs to advise what metasurface array of SOI nano-pillars gives the closest result. Utilizing a synthetic data set of devices simulated with rigorous coupled wave analysis (RCWA), we create an ANN-accelerated simulator, achieving a computational speedup of O(106) over the relatively quick RCWA simulation method. We then couple this simulator with an ANN-based predictor, which directly binds the SOI metasurfaces in question to a targeted optical performance. Together, this simulator/predictor ANNs combination provides a general framework in rapidly evaluating and designing potential optical metamaterial devices beyond what is currently possible via straightforward simulation and more standard optimization methods.