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.

使用基于无监督分割的计算机视觉方法构建的偏置三角特征提取框架，可以方便直接识别和量化其物理特性，从而避免了人力输入或大型训练数据集用于监督学习方法，并实现了逐像素形状和特征标签的自动化。特别是，通过这种方法，可以有效、高效且无需训练数据地进行Pauli自旋阻塞检测。

自动偏见三角特征提取框架