The Automated Bias Triangle Feature Extraction Framework

TL;DR

This paper presents an unsupervised, segmentation-based computer vision framework for automatic extraction and analysis of bias triangles in quantum dot stability diagrams, eliminating the need for human labeling or large datasets.

Contribution

The proposed framework enables automatic, unsupervised identification and quantification of bias triangle features, including Pauli Spin Blockade detection, without training data.

Findings

Effective and efficient bias triangle detection without training data

Automated pixelwise shape and feature labeling

Facilitates analysis of quantum dot stability diagrams

Abstract

Bias triangles represent features in stability diagrams of Quantum Dot (QD) devices, whose occurrence and property analysis are crucial indicators for spin physics. Nevertheless, challenges associated with quality and availability of data as well as the subtlety of physical phenomena of interest have hindered an automatic and bespoke analysis framework, often still relying (in part) on human labelling and verification. We introduce a feature extraction framework for bias triangles, built from unsupervised, segmentation-based computer vision methods, which facilitates the direct identification and quantification of physical properties of the former. Thereby, the need for human input or large training datasets to inform supervised learning approaches is circumvented, while additionally enabling the automation of pixelwise shape and feature labeling. In particular, we demonstrate that Pauli Spin Blockade (PSB) detection can be conducted effectively, efficiently and without any training data as a direct result of this approach.