An official website of the United States government
Recent developments in the computational automated design of electromagnetic devices, otherwise known as inverse design, have significantly enhanced the design process for nanophotonic systems. Inverse design can both reduce design time considerably and lead to high-performance, nonintuitive structures that would otherwise have been impossible to develop manually. Despite the successes enjoyed by structure optimization techniques, most approaches leverage electromagnetic solvers that require significant computational resources and suffer from slow convergence and numerical dispersion. Recently, a fast simulation and boundary-based inverse design approach based on boundary integral equations was demonstrated for two-dimensional nanophotonic problems. In this work, we introduce a new full-wave three-dimensional simulation and boundary-based optimization framework for nanophotonic devices also based on boundary integral methods, which achieves high accuracy even at coarse mesh discretizations while only requiring modest computational resources. The approach has been further accelerated by leveraging GPU computing, a sparse block-diagonal preconditioning strategy, and a matrix-free implementation of the discrete adjoint method. As a demonstration, we optimize three different devices: a 1:2 1550 nm power splitter and two nonadiabatic mode-preserving waveguide tapers. To the best of our knowledge, the tapers, which span 40 wavelengths in the silicon material, are the largest silicon photonic waveguiding devices to have been optimized using full-wave 3D solution of Maxwell’s equations.
more »
« less
3D Nanophotonic device fabrication using discrete components
Abstract Three-dimensional structure fabrication using discrete building blocks provides a versatile pathway for the creation of complex nanophotonic devices. The processing of individual components can generally support high-resolution, multiple-material, and variegated structures that are not achievable in a single step using top-down or hybrid methods. In addition, these methods are additive in nature, using minimal reagent quantities and producing little to no material waste. In this article, we review the most promising technologies that build structures using the placement of discrete components, focusing on laser-induced transfer, light-directed assembly, and inkjet printing. We discuss the underlying principles and most recent advances for each technique, as well as existing and future applications. These methods serve as adaptable platforms for the next generation of functional three-dimensional nanophotonic structures.
more »
« less
- Award ID(s):
- 1807590
- PAR ID:
- 10174069
- Date Published:
- Journal Name:
- Nanophotonics
- Volume:
- 9
- Issue:
- 6
- ISSN:
- 2192-8606
- Page Range / eLocation ID:
- 1373 to 1390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Iterative learning control (ILC) is a powerful technique to regulate repetitive systems. Additive manufacturing falls into this category by nature of its repetitive action in building three-dimensional structures in a layer-by-layer manner. In literature, spatial ILC (SILC) has been used in conjunction with additive processes to regulate single-layer structures with only one class of material. However, SILC has the unexplored potential to regulate additive manufacturing structures with multiple build materials in a three-dimensional fashion. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying initial conditions, system parameters, and surface interaction dynamics in different layers of multi-material structures. In this paper, SILC is used as a recursive control strategy to iteratively construct the feedforward signal to improve part quality of 3D structures that consist of at least two materials in a layer-by-layer manner. The system dynamics are approximated by discrete 2D spatial convolution using kernels that incorporate in-layer and layer-to-layer variations. We leverage the existing SILC models in literature and extend them to account for the iteration varying uncertainties in the plant model to capture a more reliable representation of the multi-material additive process. The feasibility of the proposed diagonal framework was demonstrated using simulation results of an electrohydrodynamic jet printing (e-jet) printing process.more » « less
-
Optical analog computation is garnering increasing attention due to its innate parallel processing capabilities, swift computational speeds, and minimal energy requirements. However, traditional optical components employed for such computations are usually bulky. Recently, there has been a substantial shift toward utilizing nanophotonic structures to downsize these bulky optical elements. Nevertheless, these nanophotonic structures are typically realized in planar subwavelength nanostructures, demanding intricate fabrication processes and presenting limitations in their numerical apertures. In this study, we present a three-layer thin-film optical coating different from the conventional Fabry–Pérot nanocavity. Our design functions as a real-time Laplacian operator for spatial differentiation, and it remarkably boasts an ultrahigh numerical aperture of up to 0.7, enabling the detected edges to be sharper and have closely matched intensities. We also experimentally demonstrate its capacity for effective edge detection. This ultracompact and facile-to-fabricate thin-film spatial differentiator holds promising prospects for applications in ultrafast optical processing and biomedical imaging.more » « less
-
Abstract Nanophotonic freeform design has the potential to push the performance of optical components to new limits, but there remains a challenge to effectively perform optimization while reliably enforcing design and manufacturing constraints. We present Neuroshaper, a framework for freeform geometric parameterization in which nanophotonic device layouts are defined using an analytic neural network representation. Neuroshaper serves as a qualitatively new way to perform shape optimization by capturing multi-scalar, freeform geometries in an overparameterized representation scheme, enabling effective optimization in a smoothened, high dimensional geometric design space. We show that Neuroshaper can enforce constraints and topology manipulation in a manner where local constraints lead to global changes in device morphology. We further show numerically and experimentally that Neuroshaper can apply to a diversity of nanophotonic devices. The versatility and capabilities of Neuroshaper reflect the ability of neural representation to augment concepts in topological design.more » « less
-
ABSTRACT The dynamic spectra of pulsars frequently exhibit diverse interference patterns, often associated with parabolic arcs in the Fourier-transformed (secondary) spectra. Our approach differs from previous ones in two ways: first, we extend beyond the traditional Fresnel–Kirchhoff method by using the Green’s function of the Helmholtz equation, i.e. we consider spherical waves originating from three-dimensional space, not from a two-dimensional screen. Secondly, the discrete structures observed in the secondary spectrum result from discrete scatterer configurations, namely plasma concentrations in the interstellar medium, and not from the selection of points by the stationary phase approximation. Through advanced numerical techniques, we model both the dynamic and secondary spectra, providing a comprehensive framework that describes all components of the latter spectra in terms of physical quantities. Additionally, we provide a thorough analytical explanation of the secondary spectrum.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1515/nanoph-2020-0161
