Optimization of Scatterometry Measurements by Enhancing with Machine Learning AM: Advanced Metrology

In this study, we introduce a machine learning approach designed to augment the conventional Rigorous Coupled-Wave Analysis (RCWA) method used in scatterometry measurements. The utility of this approach is illustrated through two practical examples. Initially, we applied it to a recess structure in trench MOSFET. Following the application of our machine learning method to the RCWA model, the recess depth measurement exhibited improved stability and uniformity across the wafer. In the second example, we measured a 2D line trench in silicon (with a depth of 22 µm); here, both the top and bottom widths are parameters of interest. We show that our machine learning based model is more robust compared to the conventional RCWA method. Our results were then cross-verified using atomic force microscopy results and cross-section Scanning Electron Microscopy data, respectively.