Deep Learning-Based Prediction of Fabrication-Process-Induced Structural Variations in Nanophotonic Devices

Abstract

The performance of integrated silicon photonic devices is sensitive to small structural variations that arise from imperfections in the nanofabrication process. This sensitivity is exacerbated for next-generation devices that require fine feature sizes to push the limits of performance. In this work, we present a deep convolutional neural network model to predict fabrication variations in planar silicon photonic devices and verify their manufacturing feasibility prior to prototyping. Our model is trained on a modest set of scanning electron microscope images of structures that experience dimensional inaccuracies stemming from combined contributions from proximity effects in lithography and loading effects in dry etching. Our model quickly and accurately predicts over/under-etching, corner rounding, filling of narrow channels and holes, and washing away of small features in a photonic device. With this, the expected performance of a device can be predicted through an extra simulation and any necessary design corrections can be made prior to fabrication.