  Precise nanofabrication represents a critical challenge to developing
semiconductor quantum-dot qubits for practical quantum computation. Here, we
design and train a convolutional neural network to interpret in-line scanning
electron micrographs and quantify qualitative features affecting device
functionality. The high-throughput strategy is exemplified by optimizing a
model lithographic process within a five-dimensional design space and by
demonstrating a new approach to address lithographic proximity effects. The
present results emphasize the benefits of machine learning for developing
robust processes, shortening development cycles, and enforcing quality control
during qubit fabrication.
