Deep learning for the design of nano-photonic structures

Abstract

These authors contributed equally to this workOur visual perception of our surroundings is ultimately limited by the diffraction-limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many breakthroughs have led to unprecedented imaging capabilities beyond the diffraction-limit, with applications in biology and nanotechnology. In this context, nano-photonics has revolutionized the field of optics in recent years by enabling the manipulation of light-matter interaction with subwavelength structures (1-3). However, despite the many advances in this field, its impact and penetration in our daily life has been hindered by a convoluted and iterative process, cycling through modeling, nanofabrication and nano-characterization. The fundamental reason is the fact that not only the prediction of the optical response is very time consuming and requires solving Maxwell's equations with dedicated numerical packages (4-6).But, more significantly, the inverse problem, i.e. designing a nanostructure with an on-demand optical response, is currently a prohibitive task even with the most advanced numerical tools due to the high nonlinearity of the problem (7-8). Here, we harness the power of Deep Learning, a new path in modern machine learning, and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach also addresses in a direct way the currently inaccessible inverse problem breaking the ground for on-demand design of optical response with applications such as sensing, imaging and also for Plasmon's mediated cancer thermotherapy.While computer science has been harnessed to address the diffraction limit in imaging and characterization on one hand (super-resolution techniques such as PALM and STORM techniques and more (9-12)) and to assist with the design process on the other hand (13-19) to date no computational technique is capable of addressing both aspects in an integrated manner.Here, we present an integrated deep learning (DL) approach and show how deep neural networks

Integrated silicon photonic device design by attractor selection mechanism based on artificial neural networks: optical coupler and asymmetric light transmitter

“…Also, a computer beat the human champion of game Go for the first time in 2016 by using neural networks [28]. Recently, the effectiveness of machine learning in designing efficient nanophotonic devices is introduced [11,29].…”

Deep learning in bioinformatics: Introduction, application, and perspective in the big data era

“…In psychology, deep learning has been used to facilitate Electroencephalogram (EEG) (the abbreviations used in this paper are summarized in Table 1) data processing [149]. Considering its application in material design [97] and quantum chemistry [135], people also believe deep learning will become a valuable tool in computational chemistry [42]. Within the computational physics field, deep learning has been shown to be able to accelerate flash calculation [91].…”

Designing nanophotonic structures using conditional deep convolutional generative adversarial networks

“…This method opens new avenues towards the development of nanophotonics by providing a fast and convenient approach to design complex nanophotonic structures that have desired optical properties.Keywords Nanophotonics · Inverse design · Conditional deep convolutional generative adversarial network · Deep learning Recently, data-driven design approaches have been proposed to overcome this problem. These approaches use artificial neural networks to design nanophotonic structures [10,11,12,13]. Previous studies first set the shape, such as multilayers [10] or H-antenna [11] of structures to be predicted, then trained NNs to provide output structural parameters that achieve desired optical properties.…”