Compressed Learning for Nanosurface Deficiency Recognition Using Angle-Resolved Scatterometry Data

Nanoscale manufacturing requires high-precision surface inspection to guarantee the quality of the produced nanostructures. For production environments, angle-resolved scatterometry offers a non-invasive and in-line compatible alternative to traditional surface inspection methods, such as scanning electron microscopy. However, angle-resolved scatterometry currently suffers from long data acquisition time. Our study addresses the issue of slow data acquisition by proposing a compressed learning framework for the accurate recognition of nanosurface deficiencies using angle-resolved scatterometry data. The framework uses the particle swarm optimization algorithm with a sampling scheme customized for scattering patterns. This combination allows the identification of optimal sampling points in scatterometry data that maximize the detection accuracy of five different levels of deficiency in ZnO nanosurfaces. The proposed method significantly reduces the amount of sampled data while maintaining a high accuracy in deficiency detection, even in noisy environments. Notably, by sampling only 1% of the data, the method achieves an accuracy of over 88%, which further improves to more than 95%—approaching the 96.4% accuracy achieved with full image classification—when the sampling rate is increased to 7%. These results demonstrate a favorable balance between data reduction and classification performance. The obtained results also show that the compressed learning framework effectively identifies critical sampling areas. The framework was further validated on GaN nanosurfaces, which confirmed that the sampling strategy generalizes effectively across different materials and achieves classification accuracies close to full-image performance.