Multimodal deep learning for automated atomic qubits fabrication in silicon

Yuwei Cui, Kunrong Wu, Ping Wu, Luyan Yang, Mingchao Duan, Guanyong Wang, Keji Shi, Yuting Lyu, Peihao Huang, Tao Xin, Zhen Tian, Guangchong Hu, Kedong Wang, Sirong Lu, Hongli Sun, Chenliang Su, Tianluo Pan, Yu He, Juan Yao

Abstract 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.