nanochip fab solutions

Semiconductor manufacturing over the past few decades has moved through several levels of technology, with each transition leading to lower costs and higher fab productivity. Now the industry is entering yet another change cycle, driven by increasing semiconductor quality requirements to meet the demands of IoT applications.

Semiconductor and display manufacturing are highly complex undertakings, but in that complexity lay many opportunities to improve the speed, efficiency, productivity and environmental impact of production tools and factory systems.

Applied Materials has led the way over the past few years in determining a big data analytics path for the semiconductor manufacturing industry. 

Leading conference on process control solutions for semiconductor and related high precision industries slated for San Antonio, Texas on October 28-31, 2019.

Manufacturers can turn periods of lower utilization to their benefit by using the available time to take proactive steps to increase the performance and productivity of their equipment and fab operations.

Developing security standards so that data analysis experts can work together to solve thorny process control challenges remains a top priority as semiconductor manufacturing moves to a smart manufacturing model.

As Moore’s Law has been stretched and the industry has become more global, more capital intensive, and more competitive, the focus of fab managers has expanded beyond device- and equipment breakthroughs to include significant advances in service technologies to boost the efficiency of semiconductor fabs.

Given today’s high market demand for semiconductor products, device manufacturers are looking for ways to maximize the output of good wafers from their fabs−and optimize their returns on invested capital.

Among the many tenets of smart manufacturing, “digital twin” solutions represent a significant opportunity for microelectronics manufacturers to leverage existing and emerging technologies to improve quality and throughput, and reduce variability and cost.