Selective Removal For Stronger Fins


By Matt Cogorno and Toshihiko Miyashita Remember when we could charge our mobile phones on a Sunday and not even think about it again until the next weekend? There was a time when battery life wasn’t even in the top ten concerns when purchasing a mobile phone. Today however, smartphones are constantly being used for computing, gaming, video streaming and other power-hungry applications, so... » read more

Multiple Approaches To Memory Challenges


As we enter the era of Big Data and Artificial Intelligence (AI), it is amazing to think about the possibilities for a truly seismic shift in the changing requirements for memory solutions. The massive amount of data humans generate every year is astounding and yet is expected to increase five-fold in the next few years from machine-generated data. Further compounding this growth is the emergin... » read more

Keeping Up Power And Performance With Cobalt


Chip designers require simultaneous improvements in “PPAC”: power, performance and area/cost (Fig. 1). Achieving these improvements is becoming increasingly difficult as classic Moore's Law scaling slows. What's needed is a new playbook for the industry consisting of new materials, new architectures, new 3D structures within the chip, new methods to shrink feature geometries, and advanced p... » read more

What’s Changing In Memory


As emerging big data and artificial intelligence (AI) applications, including machine learning, drive innovations across many industries, the issue of how to advance memory technologies to meet evolving computing requirements presents several challenges for the industry. The mainstream memory technologies, DRAM and NAND flash, have long been reliable industry workhorses, each optimized for s... » read more

A VC View Of The AI Landscape


In this blog post, I’ll highlight my takeaways from the recent AI Hardware Summit where I participated as a panelist. The conference’s focus on developing hardware accelerators for neural networks and computer vision attracted companies from across the ecosystem – AI chip startups, semiconductor companies, system vendors/OEMs, data center providers, financial services companies and VCs,... » read more

Looking For The Next Big Innovation


Never has there been more demand for “The Big Innovation” — one that moves the needle for performance, power and area-cost (PPAC) in a big way — as there is in the current era of AI and machine learning (ML). As summarized in Why AI Workloads Require New Computing Architectures, AI workloads require new architectures to process data. These workloads also call for heterogeneous comp... » read more

A New Type Of Switch


Back in July, Applied Materials announced that we’d been selected by the Defense Advanced Research Projects Agency (DARPA) to develop technology for AI. While Applied is engaged on the development of many disruptive technologies, it’s not often that we’re in a position to discuss them in early development. Thanks to the vision of DARPA’s Electronics Resurgence Initiative and their ... » read more

The Materials Side Of AI


As we enter the foundry 7nm and below technology nodes, tungsten fill for contacts has reached the physical limits of scaling and copper used in the lowest level interconnects is facing challenges on multiple fronts. Solving these issues will require a new conducting material, namely cobalt. This transition can enable continued device scaling and less power consumption per computation. Follo... » read more

The Role Of Cobalt In Enabling AI


We are on the cusp of the biggest computing wave yet — the AI era driven by Big Data. Enabling this era will require significant enhancements in processor performance and in the capacity and latency of memory. These requirements are coming at a time when the industry is being increasingly challenged by a slowdown in classic Moore’s Law scaling. What’s needed to continue driving the indust... » read more

What Else Is In A Node?


In part one of this blog, I reported on the 2018 Industry Strategy Symposium (ISS) where Dan Hutcheson of VLSI Research led a panel with representatives of Synopsys, NVIDIA, Intel, ASML and Applied Materials. The participants discussed how the industry is focused on simultaneously squeezing more capabilities from leading-nodes, inter-nodes and trailing-nodes to drive advances in computing. I to... » read more

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