BraggNet: Complex Photonic Integrated Circuit Reconstruction Using Deep Learning

We propose a deep learning model to reconstruct physical designs of complex coupled photonic systems, such as waveguide Bragg gratings, from their spectral responses for inverse design and fabrication diagnosis. Traditional reconstructing algorithms demand considerable computing resources at every query. Conversely, machine learning algorithms use most of the computing resources during the training process and provide effortless and orders-of-magnitude faster analysis in response to queries. This approach is demonstrated using silicon photonic grating-assisted, contra-directional couplers consisting of thousands of Bragg periods. The contra-directional couplers are modeled as coupled cavities, for which a transfer matrix model is used to generate a synthetic dataset comprising a strategic design parameter space. The free-form, architecture-independent model allows to include any geometries to the design parameter space. Upon proper training, the model achieves 1.4% mean absolute percentage error on device reconstruction and thus proves suitable for inverse design applications. To further show its potential for assessment of fabricated devices, another dataset is generated to emulate the fabrication conditions of a nominal design hindered by fabrication imperfections. The model is shown to reconstruct devices from experimental measurements with greater than 600-fold improvement in speed compared to the classical layer-peeling algorithm. This proves promising for data-driven processes required by Industry 4.0.