Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram

2026-04-15

AI Summary

This work addresses the bottleneck of manual charge-state tuning in scaling spin qubits based on gate-defined semiconductor quantum dots by proposing a deep learning–based automated tuning method. Leveraging a semantic segmentation network with a U-Net architecture and a MobileNetV2 encoder, the approach is applied for the first time to heterogeneous silicon quantum dot devices to automatically identify transition lines from charge stability diagrams and output gate voltages targeting single-electron occupancy regions. Evaluated via five-fold grouped cross-validation on 1,015 manually annotated diagrams, the method achieves an overall offline tuning success rate of 80.0%, with certain device designs exceeding 88%. The framework demonstrates generalization across multiple designs and wafers, offers scalable physical feature extraction, and lays the groundwork for real-time integration with cryogenic wafer probing systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin qubit technologies. We present a deep learning (DL) driven, semantic-segmentation pipeline that performs charge auto-tuning by locating transition lines in full charge stability diagrams (CSDs) and returns gate voltage targets for the single charge regime. We assemble and manually annotate a large, heterogeneous dataset of 1015 experimental CSDs measured from silicon QD devices, spanning nine design geometries, multiple wafers, and fabrication runs. A U-Net style convolutional neural network (CNN) with a MobileNetV2 encoder is trained and validated through five-fold group cross validation. Our model achieves an overall offline tuning success of 80.0% in locating the single-charge regime, with peak performance exceeding 88% for some designs. We analyze dominant failure modes and propose targeted mitigations. Finally, wide-range diagram segmentation also naturally enables scalable physic-based feature extraction that can feed back to fabrication and design workflows and outline a roadmap for real-time integration in a cryogenic wafer prober. Overall, our results show that neural network (NN) based wide-diagram segmentation is a practical step toward automated, high-throughput charge tuning for silicon QD qubits.

Problem

• quantum dots
• charge state tuning
• spin qubits
• charge stability diagrams
• automation

Innovation

• neural network segmentation
• charge stability diagram
• quantum dot auto-tuning
• FDSOI qubits
• deep learning