Efficient LDMOS Design via Transferable Surrogate Models and Multi-Objective Optimization

Abstract

Optimizing LDMOS performance requires balancing breakdown voltage (BV) and specific on-resistance ( $\text {R}_{\text {on},\text {sp}}$ ) under silicon-limit constraints. Conventional technology computer-aided design (TCAD)-based device design is time-consuming and inefficient for large parameter spaces. This work presents a machine learning (ML)-assisted framework that combines initial and fine-tuned deep neural network (DNN) surrogate models with multi-objective particle swarm optimization (MOPSO). The fine-tuned DNN adapts to a non-overlapping extended design space using only a small dataset, while the two surrogates are selectively applied during MOPSO to evaluate candidate designs, enabling significantly faster design evaluation compared to TCAD. SHAP analysis reveals consistent feature importance that aligns with the underlying device physics. The framework constructs diverse Pareto-optimal fronts, offering a scalable solution for automated LDMOS optimization under complex performance trade-offs.