Interpretable XGBoost Estimation of Memristor Mobility and Thickness

Accurate estimation of device thickness and instantaneous mobility is essential for reliable characterization and optimization of memristors. Conventional curve-fitting methods, based on idealized physical assumptions, often produce substantial errors, with relative deviations of 10%–25% for thickness and 20%–40% for mobility in nanoscale TiO₂ devices. This study presents a noise-resilient and physically interpretable XGBoost regression framework for direct estimation of thickness and instantaneous mobility from TiO₂ memristor current–voltage (I-V) data. Evaluated under realistic noise conditions—including shot, thermal, flicker, and quantization noise modeled after a Keysight B1500A analyzer—the framework achieved R² = 0.9973 for thickness and R² = 0.9383 for instantaneous mobility, with relative errors (REs) of 1.90% and 18.9%, respectively. Feature-importance analysis identified R_on, R_off, temperature, and predicted thickness as the dominant predictors, aligning with their physical roles in doped-region dynamics and ionic drift. These results demonstrate that the proposed machine learning (ML) framework provides a robust, interpretable, and scalable solution for parameter extraction from noisy I-V measurements, substantially surpassing traditional fitting approaches and enabling reliable device modeling and optimization.