Lumerical_FDTD_Solutions 8.11 High performance 3D FDTD-method Maxwell solver for the design, analysis and optimization of nanophotonic devices, processes and materials FDTD Solutions is a 3D Maxwell solver, capable of analyzing the interaction of UV, visible, and IR radiation with complicated structures employing wavelength scale features. FDTD Solutions empowers designers to confront the most challenging photonic design problems. Rapid prototyping and highly-accurate simulations reduce reliance upon costly experimental prototypes, leading to a quicker assessment of design concepts and reduced product development costs. FDTD Solutions can facilitate your success in diverse application areas, from fundamental photonics research to current industrial applications in imaging, lighting, biophotonics, photovoltaics, and many more. High performance FDTD solver Sophisticated FDTD meshing FDTD Solutions is the only dedicated nanophotonic FDTD simulator offering the combination of conformal and graded meshing Sophisticated FDTD meshing Figure 1: FDTD Solutions support graded, conformal meshing Conformal meshing, which accounts for subcell features by solving Maxwell's integral equations near structure boundaries, offers a way to obtain greater accuracy for a coarse mesh or faster and smoother convergence. Due to the 1/dx4 dependence of the simulation time on the mesh size in 3D FDTD, results with comparable accuracy can often be achieved in roughly 1/10 the time. Furthermore, Lumerical’s implementation of conformal meshing has been specially engineered to be compatible with the types of dispersive materials commonly found in photonics. Advanced material modeling FDTD Solutions allows users to model a wide range of linear, nonlinear, gain, dispersive and anisotropic materials Figure 2: Users can make use of the existing materials within the material database or define their own The material database within FDTD Solutions contains a large number of optical materials that can be used within simulation projects. Users can also create entirely new material models with popular Lorentz, Drude or Debye (or combinations thereof) forms, or they can create their own from sampled data. At optical frequencies, the dispersive nature of commonly-used materials must be taken into account in order to efficiently calculate accurate results over a wide wavelength range. Lumerical's Multi-coefficient Materials (MCMs), which makes it possible to simulate highly-dispersive materials, is a generalized approach of other approaches to modeling dispersive materials that offers greater accuracy with less computational effort. FDTD Solutions also provides a Flexible Material Plugin framework that allows users to use existing or create new nonlinear and gain materials or arbitrary complexity, and a framework for incorporating anisotropic materials like liquid crystals in simulation projects. High performance computing An optimized computational engine combined with support for concurrent and distributed computing allows you to quickly calculate the results you need High performance computing Figure 3: The Job Manager within FDTD Solutions allows you to launch multiple, independent jobs across different computers on your local area network FDTD Solutions comes with built-in support for concurrent computing, allowing you to easily run multiple simultaneous independent simulation jobs across your local computer network. High performance computing Figure 4: The FDTD engine within FDTD Solutions supports distributed computing, where a single job is processed across multiple different processing core, processors, or compute nodes within a high performance computing cluster It also supports distributed or clustered computing, where a single simulation job can be efficiently run across a high-performance computing system employing high performance interconnects. These features, together with its optimized computational engine, make FDTD Solutions one of the fastest FDTD simulation products available. Cloud computing on Amazon EC2 FDTD Solutions is the only nanophotonic simulator that is intentionally built and licensed for use on EC2 Like Lumerical’s other products, FDTD Solutions is easily and quickly accessible on the scalable infrastructure of the Amazon Elastic Compute Cloud (EC2). For more information please visit the Amazon EC2 support center. Flexible CAE design environment Back to Top Model arbitrarily complex geometries Build structures to be simulated with primitives, pre-defined compound simulation objects or define your own with imported objects Model arbitrarily complex geometries Figure 5: The complex geometry of a 3D CMOS image sensor modeled within FDTD Solutions. FDTD Solutions has a 3D CAD layout environment where structures can be built from primitive geometric objects, imported from a multilayer GDSII file or imported via an SEM image. An extensive object library contains more than 100 pre-defined compound structures including parametric surfaces, nanoparticle distributions, and photonic crystal arrays that can be added to a simulation project with the click of a button. Parametric design FDTD Solutions supports parameterized simulation objects and analyses, a key requirement for advanced design analysis and optimization Parametric design Figure 6: Parameterization of designs like this nanostructured solar cell antireflection layer makes performing parameter sweeps and design optimizations easy FDTD Solutions supports parametric structure definition. Within the 3D layout environment, structures can be grouped and parameterized in the hierarchical model view, providing designers with the flexibility and precision necessary for complex designs. Parameter sweeps, optimization & yield analysis Once a design project has been constructed, users can easy explore how that design behaves as key design parameters are varied Parameter sweeps, optimization & yield analysis Figure 7: :A nested parameter sweep makes it easy to find the optimal operating point for the design FDTD Solutions incorporates Lumerical's parameter sweep, optimization, and yield analysis toolbox. With parameter sweeps, it's easy to analyze design performance as a function of one or more variables. Alternatively, parameterized designs can be optimized using the built-in particle swarm optimization tool which can efficiently optimize for a large parameter space. The yield analysis capability, which performs Monte Carlo analysis given statistical variations of important design parameters, can help with understanding how the design performs for a specified performance metric given statistical variations in one or more design parameters.