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

TL;DR

This paper introduces a compressed learning approach that significantly reduces data acquisition time in nanosurface deficiency recognition using angle-resolved scatterometry, achieving high accuracy with minimal data sampling.

Contribution

It proposes a novel compressed learning framework with particle swarm optimization for efficient, accurate nanosurface defect detection from scatterometry data.

Findings

Achieves over 86% accuracy with only 1% data sampling.

Improves accuracy to 94% at 6% sampling rate.

Effectively identifies critical sampling points in scatterometry data.

Abstract

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 86%, which further improves to 94% when the sampling rate is increased to 6%. 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.