Rapid Charge Stability Diagram Generation from Device-level Modeling of Semiconductor Quantum Dots
Self-consistent Schr\"odinger-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.