Design of scatterometry with optoelectronic machine learning for discriminating nanohole cross-sectional structure

This paper presents a system for discriminating the verticality of nanohole sidewalls on dielectric substrates. The proposed system comprises optical filters and a compact neural network with only two input ports. The weak scattered field from the nanohole passes through the filters, and the neural network processes the intensity of the focused field. Numerical simulations demonstrate that this system achieves significantly lower error rates compared to conventional systems that use an optical microscope and a neural network. Additionally, we discuss the minimum aperture size of nanoholes that can be effectively discriminated.