Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures

Abstract As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge stability diagram (CSD) contain a fingerprint of a QD device’s capacitive environment, making these measurements useful tools for device characterization. However, manually interpreting these features is time-consuming, error-prone, and impractical at scale. Here, we present the Automated Characterization of Capacitive Environment for Testing, Optimization, and Reconstruction (ACCE n TOR) protocol for extracting underlying capacitive properties from CSDs. Our method combines machine-learning-based segmentation, geometric reconstruction, and transition tracking to identify and analyze charge transitions across large CSD datasets without manual annotation. We demonstrate this method using experimentally measured data from QD devices: a strained-germanium single-quantum-well (planar) and a strained-germanium double-quantum-well (bilayer). Unlike for planar QD devices, CSDs in bilayer germanium heterostructure exhibit a larger set of transitions, including interlayer tunneling and distinct loading lines for the vertically stacked QDs, making them a powerful testbed for automation methods. By analyzing series of CSDs, ACCE n TOR estimates physically relevant quantities, including relative lever arms and capacitive couplings, enabling rapid electrostatic characterization of QD devices.