SiPANN

Current Simulation Methods

Current simulation methods of photonic circuits include FDTD, FEM, EME and others all based on the notoriously complex Maxwell's Equations. The one things nearly all these methods have in common is extreme computational power required to run even the smallest and simplest of devices. They require numerous cores along with days or weeks of running time to estimate even the simplest of devices. These restraints have slowed and nearly halted the design of photonic circuits in many cases, since iterative design can take up to months or years for more complicated devices.

Machine Learning

Enter Machine Learning. Artificial Intelligence and Machine Learning have been some of the most popular topics in the past few years and we see these algorithms as having a potential incredible impact in the field of photonics. Using techniques such as neural networks, support vector regressions, and even simply multivariate linear regressions, the attributes of photonic circuit building blocks such as waveguides, y-branches, and directional couplers can be learned quickly and precisely for many orders of magnitude speed increases as compared to traditional methods.

Directional Coupler

Our current focus is on the directional coupler, one of the most basic building blocks of a photonic circuit. Directional couplers leverage the fact that as light travels through a waveguide (the photonic equivalent of a wire), it distributes in a bell curve shape, where the "tails" of this bell curve actually live outside of the waveguide. This means as two waveguides come close together, some of the light may leak out into the other waveguide, effectively creating a power divider.

We've worked to extend a recent model [1] that estimates coupling based on the even and odd supermodes of coupled straight waveguides. We have named this extension SCEE (simulator of photonic coupling devices based on eigenmode estimation). [2] By using multivariate linear regressions, gaussian quadrature, and extending the model to include phase tracking, SCEE accurately produces both magnitude and phase outputs of directionals couplers with arbitrary waveguide and path geometry orders of magnitude faster than current methods. SCEE is also scale invariant.

We have made SCEE, and all of photonic simulators based on machine learning methods, available in the python package SIPANN. You can learn more here.

Sources

[1] Meisam Bahadori, Mahdi Nikdast, Sébastien Rumley, Liang Yuan Dai, Natalie Janosik, Thomas Van Vaerenbergh, Alexander Gazman, Qixiang Cheng, Robert Polster, and Keren Bergman, "Design Space Exploration of Microring Resonators in Silicon Photonic Interconnects: Impact of the Ring Curvature," J. Lightwave Technol. 36, 2767-2782 (2018)

[2] Easton Potokar, R Scott Collings, Alec M Hammond, and Ryan Camacho, "Rapid Simulation of Complete Scattering Parameters for Coupled Waveguides with Arbitrary Geometries", Preprint.

[3] https://edge-ai-vision.com/2015/11/using-convolutional-neural-networks-for-image-recognition/