Abstract
Modern semiconductor manufacturing requires extreme precision as yield margins narrow in the "More-than-Moore" era. While physics-based models (PBMs) provide high-fidelity insights into plasma etching, their computational intensity-often requiring hours per simulation-renders them impractical for direct iterative optimization. This work demonstrates a hybrid framework that utilizes data-driven surrogate models to enable rapid, cost-effective process optimization. A 2D axisymmetric fluid model of an inductively coupled O2 plasma (ICP) reactor was developed to generate a training dataset for two neural architectures: a Multi-Layer Perceptron (MLP) and a Kolmogorov-Arnold Network (KAN). These surrogates predict radial etching rates across a wide operating window of power, pressure, gas flow, and bias voltage. By replacing the expensive PBM with these high-speed surrogates, derivative-free optimization algorithms (Nelder-Mead and Powell) successfully identified a profit-maximizing operating point (2000 W, 10 mTorr) orders of magnitude faster than direct physical simulation. The results confirm that surrogate-based optimization effectively captures dominant physical trends, such as ion-flux limited regimes, while providing a "Confidence Gap" through model disagreement to flag epistemic uncertainty. This methodology offers a scalable blueprint for reducing the computational burden of process design, transitioning from expensive trial-and-error to efficient, physics-validated autonomous discovery.