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arXiv · 2012.08653

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
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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 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.

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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. 2020-12-15. Optimization of Quantum-dot Qubit Fabrication via Machine Learning. https://doi.org/10.1063/5.0040967

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