The complexity of manufacturing photolithography has increased significantly. The increase in the level of integration has driven smaller feature-sized integrated circuits (ICs). The evolution in stepper technologies has been geometric. This has enabled the printing of printed ICs with a 45 nm feature size. Improvement in lithographic technology is moving towards 32 nm. This feature-size roadmap poses many challenges to semiconductor manufacturing technology. Advanced photomask synthesis, high-NA steppers, and computational lithography are some examples of the solution space. Optical proximity correction (OPC) and model-based optical proximity correction (MBOPC) are subsets of this solution space. OPC has matured significantly and is the de facto solution for manufacturing photomasks up to the 65 nm node. The OPC technique has been further refined as model-based OPC and has been applied to advanced printing technology of 45 nm. The OPC solution for 45 nm technology has limitations of mask rule check (MRC) and manufacturability restrictions. These restrictions are inevitable in OPC and MBOPC solutions because of the limits in lithographic technology. The technology evolution towards 32 nm has equally challenged the non-linear treatment of wafer-level problems in OPC solutions. PBOPC has limitations in reducing the wafer optical proximity error of the granny's issue, edge placement, mask rule check, etc. PBOPC also has limitations in reducing the mask error enhancement factor. With all these challenges, it is still a formidable solution methodology to address the wafer and mask level issues. Such a formidable solution architecture can result in a limited number of PBOPC solutions. This text looks at the performance of advanced PBOPC features on exposure tuning and the effects of higher-order wafer and aerial image effects. This text also discusses the performance of continuous process correction of masks, lenses, and scanners.

The supply chain ecosystem plays a very important role in the success or failure of organizations, markets, and economies. Supply chain ecosystems are broadly defined as supply chain organizations and their collaborators. Today's combined challenges of pandemic shutdowns, rising internet usage, and skyrocketing climate change concerns demand that the supply chain ecosystem better connect with customers, when and how they want, to provide products and services with high levels of availability and zero defects, yet collaboratively do this to reduce transportation and production risks, often at the same time reducing operational costs and carbon footprints. Addressing these challenges, this work explores the cloud delivery capabilities of cloud-native architectures to enable the big data integrations and analytics that are needed to grow smarter supply chain ecosystems. This work describes what smart supply chain ecosystems are and how they are planning to grow their technology and integration capabilities. Discussing the industry-leading advanced and manufacturing technology producer ecosystems, it is explained how their technology collaboration and investment plans are driven by climate change and job creation goals. With these background models, the work examines the new digital reality of customer-driven experiences and economies that are demanding cloud-native and intelligent technology partnerships to deliver climate objectives, operational responsiveness, and compatibility to avoid trading economies of scale for economies of integration. The final objectives of this paper are to share key ideas about the need to balance the growing customer service direct-to-consumer business models with those for collaborative investment by market and industry. In doing this, it hopes to promote an intelligent supply chain ecosystem foundation for helping its different participating countries survive and thrive in the digital economy.