Generative-adversarial-network-based dimensional measurement of optical waveguides

Abstract

We propose a high-throughput and precise waveguide-dimensional-measurement method consisting of a generative adversarial network (GAN) and curve-fitting-based dimensional calculator using sidewall functions. The GAN can learn the differences between low-magnification (LM) and high-magnification (HM) optical microscope images taken with different objective lenses at different magnifications over the same area. The LM and HM images of the waveguides are captured using an optical microscope at magnifications of 500× and 2000×, respectively. We obtained a standard deviation of the waveguide widths of approximately 0.8 pixels (∼ 42 nm), and confirmed precise width measurement using super-resolution images at the same imaging throughput as with an LM microscope.