Physics-aware Jacobian calibration for bridging the simulation-to-real gap in LDMOS modeling

The simulation-to-real gap between idealized TCAD simulations and experimental data from fabricated devices limits the accuracy of data-driven modeling for semiconductor devices. This work proposes a Physics Aware Jacobian Calibration (PAJC) framework to improve neural network (NN) surrogate modeling of LDMOS devices under sparse experimental supervision. A Physics Prior Model (PPM) trained on large scale TCAD simulations provides first order response sensitivities through its Jacobian matrix, enabling corrections from a small set of experimental anchor samples to propagate across the design space. Using the corrected simulation data together with experimental samples, a Calibrated Proxy Model (CPM) is constructed for device performance prediction. Experimental results on LDMOS devices show that the CPM achieves an average Mean Absolute Percentage Error (MAPE) of 2.28% on an unseen experimental test set while preserving physically consistent trends between design parameters and device performance. The proposed framework provides a scalable approach for physics informed AI assisted semiconductor modeling under realistic process variations.