Multimodal deep learning for automated atomic qubits fabrication in silicon

Donor-based spin qubits in silicon are a promising platform for scalable quantum computing due to their long coherence times and high-fidelity gate operations. A viable path for fabricating donor qubit arrays with atomic precision is scanning tunneling microscopy hydrogen depassivation lithography (STM-HDL). However, the identification of the intermediates of the dissociating precursors has traditionally relied on manual, expert-driven interpretation of scanning tunneling microscopy (STM) images, which severely limits throughput and impedes full automation. Here, we introduce an artificial intelligence-powered framework based on a multimodal YOLO (You Only Look Once) neural network, which performs simultaneous analysis of dual-polarity STM images acquired at positive and negative bias voltages. Our approach enables real-time automated classification of the species after dissociation of precursors and atomic-precision localization of dopant precursors during phosphorus donor qubit fabrication in silicon. The proposed multimodal architecture outperforms single-modality models, achieving 92.1% classification accuracy for surface adsorbates and 76.3% localization accuracy, representing a 30.4% improvement in overall performance. To our knowledge, this work constitutes the first demonstration of multimodal deep learning applied to STM image interpretation, offering a key step toward scalable and autonomous fabrication of atomically engineered qubit arrays in silicon.