Optimization of Quantum-dot Qubit Fabrication via Machine Learning

• URL: http://arxiv.org/abs/2012.08653v1
• Date: Tue, 15 Dec 2020 22:30:49 GMT
• Title: Optimization of Quantum-dot Qubit Fabrication via Machine Learning
• Authors: 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 summary: We train a convolutional neural network to interpret in-line scanning electron micrographs. The strategy is exemplified by optimizing a model lithographic process within a five-dimensional design space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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