pyclq: Image analysis suite for fabrication and metrology of superconducting quantum processors

Abstract

This report introduces a customized software tool to enable automation of optical inspection optical and SEM images of a superconducting quantum processor during its fabrication. This is achieved by implementing image processing algorithms using the Python package OpenCV. This suite consist of three components; pyclq_base, pyclq_jj, pyclq_ab. The first component will template match the base layer to its CAD design. The second component will be a validation for the airbridges. These have 3D components and therefore can not be matched to their CAD design. The results had about twice as many false positives for, broken bridges, and no false negatives. The third component measures the width and overlap area for Manhattanstyle Josephson junctions using two different filtering methods; k-mean segmentation and thresholding. The results of these three components are used as a tool to understand the sources of spread in the conductance of Josephson junctions therefore optimizing the fabrication process.