Semiconductors

Join Applied Materials and a panel of experts in San Francisco on Dec. 4 to explore the types of memories needed for artificial intelligence applications.

New facility will offer the industry’s most advanced R&D environment for working with the extended semiconductor ecosystem to accelerate readiness of new materials, methods and devices for computing applications including Big Data and Artificial Intelligence.

Applied Materials and National University of Singapore jointly open a new R&D lab with support of the National Research Foundation Singapore. The goal of the collaboration is to accelerate the discovery and commercialization of new materials for manufacturing next-generation semiconductors.

AI is one of the most exciting and most pervasive inflections to happen in our lifetime. But it is challenging the entire design ecosystem – from materials to systems – in a way that we haven’t experienced before.

Advanced packaging can meet performance, power and density requirements for a range of More-than-Moore device types.

The Applied Materials platforms that create our Integrated Materials Solution for cobalt have been industry workhorses for several decades, and the innovations being developed show they remain critical for the dimensional and materials scaling necessary to continue improving the power, performance and area/cost (PPAC) of chips.

New materials and integrated process approaches offered on the Producer platform will help to enable future semiconductor designs.

My previous blog explained the computing architecture requirements for AI workloads. Now, I take a deep dive into the types of materials engineering breakthroughs needed to enable these new architectures.

This two-part blog series examines the changes needed to computing architectures in the AI era and the role materials engineering will play to make it happen.