Artificial intelligence (AI) benefits many industries but is limited by the so-called “curse of dimensionality,” which arises when machine learning models handle data with many features. This issue is particularly relevant in semiconductor manufacturing, where process parameters such as temperature, time, and thickness define a high-dimensional data space. Increased dimensionality exponentially expands the volume of this space, causing data points to become sparse and traditional machine learning models to struggle with capturing complex correlations.