  The sensitivity of thin-film materials and devices to defects motivates
extensive research into the optimization of film morphology. This research
could be accelerated by automated experiments that characterize the response of
film morphology to synthesis conditions. Optical imaging can resolve
morphological defects in thin films and is readily integrated into automated
experiments but the large volumes of images produced by such systems require
automated analysis. Existing approaches to automatically analyzing film
morphologies in optical images require application-specific customization by
software experts and are not robust to changes in image content or imaging
conditions. Here we present a versatile convolutional neural network (CNN) for
thin-film image analysis which can identify and quantify the extent of a
variety of defects and is applicable to multiple materials and imaging
conditions. This CNN is readily adapted to new thin-film image analysis tasks
and will facilitate the use of imaging in automated thin-film research systems.
