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- W2896233414 abstract "We propose and demonstrate the first end-to-end artificial neural network (ANN) modeler for the automated design of photonic systems and devices. This approach gathers an initial range-restricted batch of numerically solved electromagnetic data and maps the nonlinear input-output relationship into a linear model of learned weights. This model is used to predict the output of different device variations for orders-of-magnitude faster optimization or system-level simulations. Our implementation uses the MATLAB numerical computing environment with the finite-difference time-domain electromagnetic solver from Lumerical to acquire the device data, create and train the ANN model, and optimize for a desired device output. In this demonstration, we create a model for a silicon grating coupler, which computes 56,000X faster than the numerical simulation, with an accuracy greater than 97% of the numerical results. Using a parametric sweep or an inverted ANN, the device parameters can be immediately found for a desired output." @default.
- W2896233414 created "2018-10-26" @default.
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- W2896233414 date "2018-08-01" @default.
- W2896233414 modified "2023-09-27" @default.
- W2896233414 title "Automating Photonic Design with Machine Learning" @default.
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- W2896233414 doi "https://doi.org/10.1109/group4.2018.8478722" @default.
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