nanochip fab solutions

Moore’s Law follows the continual advancement of smaller silicon transistors, which is ideal for faster operating speeds in processors and increasing memory size density.

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

In an industry as demanding as semiconductor manufacturing, where globalization, capital investments, and technical complexity continue to accelerate, the availability of service parts inventory to ensure equipment availability is a critical success factor.

When Ellie Yieh, fresh out of the University of California at Berkeley’s chemical engineering program, joined Applied Materials 30 years ago, the semiconductor industry was poised to make a series of fundamental technology changes.

When the conversation turns to cooperative semiconductor R&D centers, Applied Materials’ Maydan Technology Center (MTC) in Silicon Valley may not immediately come to mind. But MTC is becoming a busy hub where semiconductor customers work cooperatively with Applied on an ever-widening variety of technical challenges, from logic and memory to emerging memories for AI, IoT and other areas.

Fundamental changes are occurring in the display industry, driven by demands for higher-resolution screens and other capabilities for both mobile and TV applications.

Introduced in 1985, the ≤200mm silicon wafer generation has enjoyed longevity that is nothing short of amazing. More than Moore (MtM) devices such as MEMs sensors have expanded in variety and number, driven largely by smartphones.

In semiconductor front-end fabs as well as back-end assembly, test and packaging (ATP) facilities, efficient and flexible factory-floor production dispatching is vital to high productivity. Dispatching is the process of determining the next job that should be processed—ideally in real time—then assigning it to the right tool or station and ensuring it gets there at the right time and with minimum queue time.

Discussions about advanced process control (APC) often put the algorithms in the forefront and the engineer in the background. With deep learning receiving so much recent attention, people are understandably fascinated by its claimed potential to extract the value inherent in the data amassed by sensors, metrology tools and other sources.