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nanochip fab solutions

Winter 2018

Ali Salehpour

A Letter from Ali Salehpour - Many Roads to Accelerate Time to Market

In ancient times, people used to say that all roads lead to Rome, then the commercial center of one of the world’s greatest empires. With the globalization of today’s technology-based economy, we might well say that all roads lead to market.

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Boost Factory Productivity Faster With Applied SmartFactory® Dispatching And Reporting

BY Madhav Kidambi

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.

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Breaking The Cycle—MtM Technologies Push Forward

By Nanochip Staff

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.

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In The Age of AI, Maydan Technology Center Evolves

In The Age of AI, Maydan Technology Center Evolves

BY David Lammers

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.

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E-Beam Review and CD Measurement Revolutionizes Display Yield Management

By Kerry Cunningham

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

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The Last Word

By David Lammers

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.

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