Process window and defect monitoring using high-throughput e-beam inspection guided by computational hot spot detection

Wang, Fei; Zhang, Pengcheng; Fang, Wei; Liu, Kevin; Jau, Jack; Wang, Lester; Wan, Alex H. L.; Hunsche, S.; Halder, Sandip; Leray, Philippe

doi:10.1117/12.2219515

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Optimization of quantum-dot qubit fabrication via machine learning

Mei

Milosavljevic

Simpson

et al. 2021

Applied Physics Letters

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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.

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