Models
JCMsuite solves time-harmonic Maxwells equations for

• Optical scattering problems
• Optical waveguide design problems,
• Optical resonance problems,
  as well as
• Linear elasticity problems,
• Heat conduction problems,
  and any coupled problem classes of these types.

JCMsuite allows for any complex and anisotropic material permittivity and permeability tensors.

Mathematical Background
JCMsuite is based on advanced mathematical methods and technologies from computer science.

• High order vectorial finite elements
• Exact 2D and 3D geometry modelling using unstructured meshes
• Self-adaptive grid refinement relying on residual-based error estimation
• Combination of different mesh refinement levels and polynomial ansatz functions of varying degree
• 1D, 2D, 3D Cartesian coordinatesystems, cylindrically symmetric, mirror symmetric and twisted coordinate systems
• Rigorous treatment of periodic and transparent boundaries
• Automatic computation of first- and higher-order parameter derivatives

Easy-to-use data access and processing
JCMsuite is designed to fully integrate into your MATLAB®/Octave or Python environment. The entire design task can, besides running stand alone, be embedded into these high-level scripting languages, enabling an intuitive, comfortable and flexible scripting of complicated design setups via
MATLAB®/Octave-, Python-, C-interfacing.

Various post-processes for the analysis of simulation results (e.g., Poynting flux, Fourier transformation, optical imaging) are included.

• JCMsuite - Complete. Rigorous. Fast.
• JCMsuite is a powerful and flexible simulation software best suited for the simulation and design of complex nano-optical systems. → Application Areas
  • JCMsuite provides convenient graphical user interfaces and easily integrates with your favoured scripting environment. → Modules
  • It leverages state-of-the-art techniques and provides faster and more accurate numerical solvers for optical, continuum mechanics and heat conduction problems. → Technology
  • It is fully integrated in a data analysis toolkit to optimize your optical systems with latest machine-learning technology. → Analysis and Optimization Toolkit

• Learn more about its Use in Research and Development.
• Learn how to Get Started using JCMsuite.
•  Download JCMsuite

JCMsuite is based on advanced mathematical methods and technologies from computer science. It leverages the power and flexibility of the Finite Element Method (FEM) to achieve fast and accurate results and uses latest machine-learning technologies to optimize complex optical devices.

CAD and meshing tools

• The JCMsuite geometry and meshing tools are specially desinged for photonic applications.
  • Shapes and geometries: Various CAD geometries such as 2D and 3D primitives, extrusions, corner-rounded shapes, and free-form shapes can be created using linear or curved elements.
  • Symmetries: By defining periodic or mirror-symmetric meshes or by working in cylindrical and twisted coordinate systems the computation times can be drastically reduced.
  • Infinite structures: Multi-layers, layered exterior domains and waveguide structures are supported.
  • Adaptive meshes: automatic mesh refinements, corner and normal refinements allow for highly accurate computations.
• Learn more in our tutorials:  Geometry Setup and Mesh Generation
• Related blog posts:  Advanced Finite Element Methods

Hp-FEM solver

• The Finite Element Method (FEM) provides a general, rigorous, versatile, and very fast method to the solution of scientific and technological challenges.
  • Problem classes: JCMsuite solves the time-harmonic Maxwell's equations for optical scattering problems, waveguide design problems, and optical resonance problems as well as linear elasticity problems, heat conduction problems, and any coupled problem classes of these types.
  • Automatic numerical settings: Various numerical settings such as finite-element degrees and PML settings (perfectly matching layer) are chosen automatically relying on residual-based error estimation.
  • Materials and sources: Various material properties, such as complex and anisotropic material permittivity and permeability tensors, dispersion properties, thermal conductivity, and stiffness can be defined. The structures can be excited, for example, by plane waves, periodic or isolated dipoles, beams, and waveguide modes.
  • Post processes: A special focus lies on the support and efficient computation of all necessary post processes in optics such as Fourier transformation, far fields, energy fluxes, overlap integrals, optical imaging, resonance expansions, and Purcell-factors.
• Learn more in our tutorials:  Electromagnetic Field Computation
• Related publications:  Light Scattering Computation  Propagation Mode Computation  Resonance Mode Computation  Advanced Finite Element Methods
• Related blog posts:  Light Scattering Computation

Analysis and optimization toolkit

• Machine learning technologies enable the efficient analysis and optimization of the properties of optical devices.
  • Optimization: Bayesian optimization is a highly efficient optimization method that enables to develop high-performance devices in shorter computation times. Other supported optimization methods include downhill simplex optimization, particle swarm optimization, differential evolution, and the L-BFGS-B method.
  • Uncertainty quantification: Often, paramerters of optical systems are subject to uncertainties and fluctuations. The toolkit includes several efficient methods to determine parameter sensitivities (Sobol coefficients) and the average performance under fluctuations and its variance.
  • Parameter reconstruction: Reconstructing system parameters like material properties and shape parameters from measured data is a complex numerical task. JCMsuite includes dedicated tools for the time efficient and precise reconstruction of parameter values and their measurement uncertainties.
  • Prediction: After a learning phase the performance of optical devices can be predicted for unknown parameters.
• Documentation:  Matlab® interface,  Python interface
• Related publications:  Optimization and Parameter Retrieval Methods  Uncertainty Quantification Methods
• Related blog posts:  Optimization and Parameter Retrieval Methods

JCMsuite has intuitive and flexible graphical and scripting interfaces for setting up projects and inspecting simulation results. Complex optimization runs can be tracked via a powerfull dashboard. For computationally demanding simulation tasks, cloud computing resources can be integrated with ease.

Graphical User Interfaces

JCMsuite offers graphical user interfaces to control, edit and view simulations projects and results:

• With JCMcontrol one can easily setup simulation projects including layout descriptions, source definitions, material definitions, numerical setting and post processes.
• JCMview is an interactive 3D viewer specially designed to inspect meshed layouts and electromagnetic field distributions.

 How to get started  Download and try for free

Scripting Interfaces

• JCMsuite is designed to fully integrate into your MATLAB®/Octave or Python environment. This enables an intuitive, comfortable and flexible scripting of complicated design setups via MATLAB®/Octave, Python, and C-interfacing.

• Scripts for common data analysis task, such as running and vizualizing parameter scans, can be automatically generated within JCMcontrol.

•  JCMsuite’s Matlab® guide  JCMsuite’s Python guide

Analysis and Optimization Toolkit

• Complex analysis and optimization tasks can be easily set up using high-level Python or Matlab® commands from JCMsuite's analysis and optimization toolbox. The scripts for these tasks can be also automatically generated using JCMcontrol. The toolbox integrates an interactive dashboard that vizualises the progress and results of the numerical studies.

•  Matlab® toolkit interface  Python toolkit interface

Computing on Remote Workstations or Cloud Resources

• JCMsuite provides seamless integration of computational resources from remote workstation or computation clusters to scalable compute capacities in the cloud provided by Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure.

•  Using cloud resources from Matlab® Using cloud resources from Python