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

TL;DR

This paper introduces an automated method combining machine learning and image processing to analyze charge stability diagrams of quantum dot devices, enabling rapid and accurate extraction of capacitive properties crucial for device development.

Contribution

The authors develop a novel automated protocol for analyzing charge stability diagrams using machine learning, significantly reducing manual effort and error in characterizing complex quantum dot devices.

Findings

Successfully applied to strained-germanium quantum dot devices

Able to identify and track charge transitions automatically

Provides statistical estimates of capacitive parameters

Abstract

As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device 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 an automated protocol for extracting underlying capacitive properties from CSDs. Our method integrates machine learning, image processing, and object detection to identify and track charge transitions across large datasets without manual labeling. We demonstrate this method using experimentally measured data from a strained-germanium single-quantum-well (planar) and a strained-germanium double-quantum-well (bilayer) QD device. 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 the properties of many CSDs, we can statistically estimate physically relevant quantities, like relative lever arms and capacitive couplings. Thus, our protocol enables rapid extraction of useful, nontrivial information about QD devices.