AI 中文总结
本研究结合MDMM方法与自动调优程序，通过少量自洽模拟及FCI能量计算参数化哈伯德模型，低成本快速生成半导体量子点器件电荷稳定图，可扩展至多架构，与实验数据定性一致。
AI 中文摘要
自洽薛定谔-泊松计算是预测层状半导体量子点器件行为的有力工具，但通过全模拟栅极电压扫描表征电荷稳定图的计算成本极高。我们将多域多模型（MDMM）方法与自动调优程序结合，识别与选定电荷构型相关的栅极电压；这组少量自洽模拟可辅以全组态相互作用（FCI）能量计算，提取充电能、杠杆臂和点间库仑相互作用，直接参数化哈伯德模型以快速生成电荷稳定图。针对英特尔Tunnel Falls Si/SiGe器件，我们证明哈伯德模型生成电荷稳定图的计算成本仅为电压偏置扫描的一小部分，还将模拟图与实验数据对比，显示定性一致。该成果是迈向半导体量子点器件预测性数字孪生模型的一步，我们还将该工作流应用于另一器件的杠杆臂工程，证明方法可扩展至多架构。
英文摘要
Self-consistent Schrödinger-Poisson calculations are a powerful tool for predicting the behavior of layered semiconductor quantum dot devices. However, characterization of charge stability diagrams through fully simulated gate-voltage sweeps is computationally expensive. Combining a Multi-Domain Multi-Model (MDMM) approach with an automated tuning routine, we identify gate voltages associated with selected charge configurations. This small set of self-consistent simulations can be augmented with Full Configuration Interaction (FCI) energy calculations to extract charging energies, lever arms, and interdot Coulomb interactions to directly parameterize a Hubbard model for rapid charge stability diagram generation. For an Intel Tunnel Falls Si/SiGe device, we demonstrate the Hubbard model's ability to reproduce charge stability diagrams at a fraction of the computational cost in comparison to voltage bias sweeps. We further compare the simulated diagrams to experimental data and demonstrate qualitative agreement. Our result represents a step towards predictive digital twin models for semiconductor quantum dot devices. Finally, we apply this workflow towards lever arm engineering in a second device, demonstrating that the method extends to multiple architectures.
Comments15 pages, 9 figures
