Learn a comprehensive set of simulation techniques to predict the performance of photonic nanostructures. This engineering course is an introduction to photonic materials and devices structured on the wavelength scale. Generally, these systems will be characterized as having critical dimensions at the nanometer scale. These can include nanophotonic, plasmonic, and metamaterial components and systems.
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This course may be useful for advanced undergraduates with the prerequisites listed below; graduate students interested in incorporating these techniques into their thesis research; and practicing scientists and engineers developing new experiments or products based on these ideas.
This course is part of the Nanoscience and Technology MicroMasters Program.
What you'll learn:
- Photonic bandstructures
- Transfer matrices
- Time-domain simulations
- Finite-element methods
Syllabus
Week 1: Photonic Bandstructures
- Bloch Theorem
- 1D Bandstructures
- 2D Bandstructures
- Photonic Crystals
Week 2: Photonic Bandstructures (continued)
- Photonic Crystals
- Photonic Bandstructure
- Simulation using MIT Photonic Bands (MPB)
Week 3: Transfer Matrices
- Ray Optical Matrices
- Wave Optics Transfer Matrices
- Wave Optics S-Matrices
- Photonic Simulations
- CAMFR
- Metasurfaces
Week 4: Time-Domain Simulations
- Finite Difference Time Domain Method
- MEEP: An FDTD Solver
- Light Trapping in Photovoltaics
- Using MEEP
- MEEP Resonators
- MEEP: Photonic Bandstructures
- FDTD Validation Against Experiment
- Local Density of States
Week 5: Finite-Element Methods
- Simulating Bandstructures in FDTD
- Beam Propagation Method
- Finite Element Method (FEM)
- An FEM Waveguide Mode Solver
- Thermal Transport
- FEM Modeling
- Blackbody Radiation
Prerequisites:
- This course is intended for audiences with a background in the physical sciences or engineering.
- Basic familiarity with the principles of Maxwell’s equations, covered in a first year class on physics, is needed.
- Some working knowledge of integral and vector calculus, as well as basic linear algebra, is assumed.
- Prior experience with basic programming techniques and algorithms is useful but not strictly required; pointers to web-based resources covering these background topics will be available.
