AI-guided OCD metrology for single HAR sub-micron via measurement

Abstract

Global semiconductor packaging manufacturers are developing advanced process technologies with the rapid rise of heterogeneous packaging and 3D packaging. High-aspect-ratio (HAR) structures like through silicon vias (TSV) or redistribution layers (RDL) that come with the prevalence of 3D packaging technology have further significantly increased the difficulties in optical critical dimension (OCD) metrology. Due to emerging technical challenges, effective sub-micron HAR OCD solutions are highly demanded to resolve the technical bottleneck. Thus, This article presents an AI-guided method for simulating and building a training dataset using the finite difference time domain (FDTD), then forming a DNN model for reconstructing CDs. At the same time, an optical scatterometry-based microscope was developed to adopt optical light capable of penetrating a sub-micron opening size structure and characterizing critical dimensions such as top critical dimension (TCD) and depth. In the optical system design, the optical FOV can be narrowed down only to cover a single sub-micron structure for OCD metrology. A preliminary test verified that a single sub-micron structure with an aspect ratio of 1:3.3 and the maximum bias between the measured data and the SEM references could be kept within a few tens of nanometers for its depth, TCD, and BCD measurement.