What’s Next For AI, Quantum Chips?

What’s Next For AI, Quantum Chips?

Leaders of three R&D organizations, Imec, Leti and SRC, discuss the latest chip trends in AI, packaging and quantum computing.

January 24th, 2019 – By: Mark LaPedus

Semiconductor Engineering sat down to discuss the latest R&D trends with Luc Van den hove, president and chief executive of Imec; Emmanuel Sabonnadière, chief executive of Leti; and An Chen, executive director for the Nanoelectronics Research Initiative at the Semiconductor Research Corp. (SRC). Chen is on assignment from IBM. What follows are excerpts of those conversations, which took place as a series of one-on-one interviews.

SE: What are some of the big challenges on the R&D front?

Van der Hove: In terms of the technology challenges, it’s clear that the node-to-node transition is getting more and more difficult. To a large extent in our discussions, we come down to lithography. We know EUV has been slow in moving into production. As a result, the technology becomes more complex. With all of these multi-patterning options, the cycle times become longer. So it has become hard to scale. Having said that, I’m convinced that we are not giving up on scaling. With EUV coming on line in early 2019, we are going to see a re-acceleration of that, because of the lithography capability. Now, having said that, it’s clear that it’s not the same story as it was 10 years ago. We need to link the systems view to steer the technology development. It’s not a one-dimensional roadmap anymore. We will see a diversification of technologies, depending on which part of the system we are looking at and which part of the system we want to scale. So we need a portfolio of technologies, which we need to combine. So, one device is not ideal for everything in a system.

Sabonnadière: We are in front of some big things. For big things, you need collaboration. In the past, the idea was, I do it on my own and hide what I’m working on because I want to be on top. Now, that’s over. With the new geopolitical situation, we have to rethink how we collaborate at different levels. That’s what we are pushing today. The big challenge is to collaborate more.

Chen: People continue to push scaling, but it’s obvious that we really can’t push that much further. For many, scaling is not really a key driver. Many are now looking at new computing paradigms and new functions like AI and quantum computing.

SE: Artificial intelligence (AI) is a hot topic. One part of AI is called machine learning. Machine learning makes use of a neural network in a system. In neural networks, the system crunches data and identifies patterns. It matches certain patterns and learns which of those attributes are important. How is AI and machine learning changing the way we look at chips and systems?

Van den Hove: In machine learning, you really need to understand the systems part. But the solutions also have to come from the technology side. We have to come up with new architectures for these AI processors or accelerators, which, for example, have a lot of embedded memory. So we are developing specific MRAM solutions that allow us to store the weights right into the processor. These are technology optimizations, but you need to understand what are the system demands. And they are different for these applications compared to others. We are also working on these neuromorphic computing engines, where we will use some of these of new emerging memories like phase-change or FeFET-based memories.

Sabonnadière: I see an explosion of data generation. AI is a solution for that. It will become much more powerful than what we can imagine today. We see that AI will resolve difficult problems. This is a big story at Leti. We have reassigned a lot of experts to AI. It’s a multi-disciplinary story. We are working on different layers of the device. Spiking memories is one part. We’ve know about this technology for several years. We are also equipped to push and create more momentum around edge AI. Everyone is taking about this edge AI story.

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