Abstract
Abstract
Dry etching and deposition of Si and Si dielectric films are critical processes for achieving high performance in advanced CMOS devices. To accurately predict and control fluctuations in these process properties during mass production, it is essential that the dry process simulation technology considers various factors. These include fluctuations in plasma–chamber wall interaction, effects of by-products on critical dimensions, Si recess dependence on wafer open area ratios and local pattern structures, the time-dependent distribution of plasma-induced damage associated with feature-scale profiles, and film properties such as density, permeability, and adhesion. Addressing these considerations can overcome issues with conventional simulations that lack the accuracy required for mass production. This paper reviews these advanced simulation technologies and discusses the perspective of the fusion of physical models with machine learning, incorporating real-time monitoring in manufacturing equipment, known as process informatics. This approach is anticipated to usher in the era of full digital twins.