Optimization of quantum-dot qubit fabrication via machine learning
Antonio B. Mei, Ivan Milosavljevic, Amanda L. Simpson, Valerie A. Smetanka, Colin P. Feeney, Shay M. Seguin, Sieu D. Ha, Wonill Ha, Matthew D. Reed
Abstract
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 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 robust approach to address lithographic proximity effects. The results emphasize the benefits of machine learning for developing stable processes, shortening development cycles, and enforcing quality control during qubit fabrication.