Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
By Alexander Piggott, Jesse Lu, K. Lagoudakis, J. Petykiewicz, T. Babinec, Jelena Vuckovic. Nature Photonics, 2015
990 citations recorded by the source corpus.
An on-chip integrated wavelength demultiplexer designed using an inverse computational algorithm is experimentally demonstrated. 1,300 and 1,550 nm wavelength light is sorted in a device area of just 2.8 × 2.8 μm2. Integrated photonic devices are poised to play a key role in a wide variety of applications, ranging from optical interconnects1 and sensors2 to quantum computing3. However, only a small library of semi-analytically designed devices is currently known4. Here, we demonstrate the use of an inverse design method that explores the full design space of fabricable devices and allows us to design devices with previously unattainable functionality, higher performance and robustness, and smaller footprints than conventional devices5. We have designed a silicon wavelength demultiplexer that splits 1,300 nm and 1,550 nm light from an input waveguide into two output waveguides, and fabricated and characterized several devices. The devices display low insertion loss (∼2 dB), low crosstalk (<−11 dB) and wide bandwidths (>100 nm). The device footprint is 2.8 × 2.8 μm2, making this the smallest dielectric wavelength splitter.
Subfields: Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/605ead4ca46f66016a3ab4bbf6049f51b8e61755
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In Atomic and Molecular Physics, and Optics
In Electrical and Electronic Engineering
Citations in the corpus
- Global optimization of dielectric metasurfaces using a physics-driven neural network (2019, methodology): “These methods range from heuristic swarm 13 and genetic algorithms 14,15 to adjoint-based topology optimization, 16,17 and they have led to metagratings, 18,19 metasurfaces, 20–22 and other nanophotonic devices 23–25 with exceptional performance.”
- Ultra‐broadband nanophotonic beamsplitter using an anisotropic sub‐wavelength metamaterial (2016, background): “This idea has ushered in breakthrough devices for fiber-to-chip coupling [27, 28], wavelength multiplexing [29], and polarization splitting [30], among other innovations.”
- Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures (2017, neutral): “Notable examples include evolutionary algorithms 1 , level set methods 2 , adjoint methods 3 , and the optimization of specific geometric parameters 4–6 .”
- Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy (2019, background): “design approaches start from the opposite end, and try to optimize certain objective functions describing the desired performance [24, 25].”