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An IBM Breakthrough Ensures Silicon Will Keep Shrinking

An IBM Breakthrough Ensures Silicon Will Keep Shrinking | cross pond high tech | Scoop.it

The limits of silicon have not been reached quite yet.

Today, an IBM-led group of researchers have detailed a breakthrough transistor design, one that will enable processors to continue their Moore’s Law march toward smaller, more affordable iterations. Better still? They achieved it not with carbon nanotubes or some other theoretical solution, but with an inventive new process that actually works, and should scale up to the demands of mass manufacturing within several years.

That should also, conveniently enough, be just in time to power the self-driving cars, on-board artificial intelligence, and 5G sensors that comprise the ambitions of nearly every major tech player today—which was no sure thing.

5nm Or Bust

For decades, the semiconductor industry has obsessed over smallness, and for good reason. The more transistors you can squeeze into a chip, the more speed and power efficiency gains you reap, at lower cost. The famed Moore’s Law is simply the observation made by Intel co-founder Gordon Moore, in 1965, that the number of transistors had doubled every year. In 1975, Moore revised that estimate to every two years. While the industry has fallen off of that pace, it still regularly finds ways to shrink.

Doing so has required no shortage of inventiveness. The last major breakthrough came in 2009, when researchers detailed a new type of transistor design called FinFET. The first manufacturing of a FinFET transistor design in 2012 gave the industry a much-needed boost, enabling processors made on a 22-nanometer process. FinFET was a revolutionary step in its own right, and the first major shift in transistor structure in decades. Its key insight was to use a 3-D structure to control electric current, rather than the 2-D “planar” system of years past.

”Fundamentally, FinFET structure is a single rectangle, with the three sides of the structure covered in gates,” says Mukesh Khare, vice president of semiconductor research for IBM Research. Think of the transistor as a switch; applying different voltages to the gate turns the transistor “on” or “off.” Having three sides surrounded by gates maximizes the amount of current flowing in the “on” state, for performance gains, and minimizes the amount of leakage in the “off” state, which improves efficiency.

But just five years later, those gains already threaten to run dry. “The problem with FinFET is it’s running out of steam,” says Dan Hutcheson, CEO of VLSI Research, which focuses on semiconductor manufacturing. While FinFET underpins today’s bleeding-edge 10nm process chips, and should be sufficient for 7nm as well, the fun stops there. “Around 5nm, in order to keep the scaling and transistor working, we need to move to a different structure,” Hutcheson says.

Enter IBM. Rather than FinFET’s vertical fin structure, the company—along with research partners GlobalFoundries and Samsung—has gone horizontal, layering silicon nanosheets in a way that effectively results in a fourth gate.

 

“You can imagine that FinFET is now turned sideways, and stacked on top of each other,” says Khare. For a sense of scale, in this architecture electrical signals pass through a switch that’s the width of two or three strands of DNA.

“It’s a big development,” says Hutcheson. “If I can make the transistor smaller, I get more transistors in the same area, which means I get more compute power in the same area.” In this case, that number leaps from 20 billion transistors in a 7nm process to 30 billion on a 5nm process, fingernail-sized chip. IBM pegs the gains at either 40 percent better performance at the same power, or 75 percent reduction in power at the same efficiency.

Just in Time

The timing couldn’t be better.

Actual processors built off of this new structure aren’t expected to hit the market until 2019 at the earliest. But that roughly lines up with industry estimates for broader adoption of everything from self-driving cars to 5G, innovations that can’t scale without a functional 5nm process in place.

“The world’s sitting on this stuff, artificial intelligence, self-driving cars. They’re all highly dependent on more efficient computing power. That only comes from this type of technology,” says Hutcheson. “Without this, we stop.”

Take self-driving cars as a specific example. They may work well enough today, but they also require tens of thousands of dollars worth of chips to function, an impractical added cost for a mainstream product. A 5nm process drives those expenses way down. Think, too, of always-on IoT sensors that will collect constant streams of data in a 5G world. Or more practically, think of smartphones that can last two or three days on a charge rather than one, with roughly the same-sized battery. And that’s before you hit the categories that no one’s even thought of yet.

“The economic value that Moore’s Law generates is unquestionable. That’s where innovations such as this one come into play, to extend scaling not by traditional ways but coming up with innovative structures,” says Khare.

Widespread adoption of many of those technologies is still years away. And success in all of them will require a confluence of both technological and regulatory progress. At least when they get there, though, the tiny chips that make it all work will be right there waiting for them.

Philippe J DEWOST's insight:

"I'm not dead. I'm getting better" - Moore's Law & the Holy Grail according to IBM

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Matériel › Graphène : bientôt des processeurs très basse consommation › GreenIT.fr

Matériel › Graphène : bientôt des processeurs très basse consommation › GreenIT.fr | cross pond high tech | Scoop.it

Depuis sa découverte en 2004, qui permit à Andre Geim, chercheur au département de physique de l’université de Manchester, d’obtenir le prix Nobel de physique, le graphène concentre toute l’attention des fabricants de semi-conducteurs. En effet, ce cristal de carbone possède trois propriétés physiques très intéressantes qui le place en première position sur la liste des alternatives au silicium pour la production de puces informatiques...

Philippe J DEWOST's insight:

Tout ce que vous avez toujours voulu savoir sur le potentiel du graphène sans oser le demander... Excellent article qui confirme la prédiction faite par Ron Dennis à la G8 Innovation Conference de Londres l'an dernier : avec le Big Data, le graphène est une des piliers fondamentaux de l'avenir de la High Tech.

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Predicting the Future and Exponential Growth

Predicting the Future and Exponential Growth | cross pond high tech | Scoop.it
“How many times would I have to fold a sheet of paper for the height of the folded paper to reach the moon?”

Human beings have terrible intuition for exponential growth. If I asked you how many times you would have to fold a single sheet of US Letter paper to reach the moon, it would be difficult to intuitively comprehend that it only takes twenty folds to reach Mount Everest, forty-two folds to the moon, and fifty to reach the sun.

Compound interest is the eighth wonder of the world. He who understands it, earns it … he who doesn’t … pays it.” –Albert Einstein
Often times when looking at the future, we use the projection of the past to predict the outcome of the future. This suggestion is generally not understood properly as due to the natural linearity of time, people are more likely to look at the next five years assuming the growth will march linearly like the past five years.

The truth is that even from the last five years to now, growth particularly technological growth is never linear and is always exponential.

The Apollo Guidance Computer, for example, was built of 4100 integrated circuits, each of which was a 3 input gate for a total of approximately 12,300 transistors – having its performance clocking in at 41.6 instructions per second.

An iPhone 6 has 3.36 billion instructions per second meaning an iPhone 6 is 80 million times faster than Apollo on just instructions per second and around 120 million times faster than the guidance computer that put Neil Armstrong and Buzz Aldrin on the moon. A single iPhone 6 could theoretically guide 120 million Apollo rockets at the same time (an iPhone 6S is 70% faster than an iPhone 6). This also doesn’t include the fact that a 64 bit processor can use less operations to process more complex computations - so 120 million times faster is definitely an understatement.

So how can we explain this type of growth?
Philippe J DEWOST's insight:

We humans are desperately linear when it comes to aiming at the future. It took our societies thousands of years to switch from Mircea Eliades "myth of eternal return" way of looking at the world (after all, nature is mostly cyclical in its immediate perception) and unfold towards the "time arrow" paradigm we are currently living in.


Except that for the past decades, something different has been emerging, that involves exponential thinking as per illustrated in Ray Kurzweil "second half of the chessboard" metaphor (echoing a legend that has its local version in almost chess playing culture).


This post explains very well what exponential thinking is about, and why we humans are so often wrong in the way we try to define the future on 10-15 years time frames. Must read especially if you feel uncomfortable with all this tech popping out and surprising us (and our governments) for the past few years. AirBnB and Uber were indeed unforeseeable and here is why...

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Mobile Moore: The New iPhone Is 40X Faster Than The Original iPhone

Mobile Moore: The New iPhone Is 40X Faster Than The Original iPhone | cross pond high tech | Scoop.it
An impressive explosion in speed: In the six years since the original iPhone was released, the speed of the iPhone has increased by 40X. The iPhone 5S alone is a doubling in speed over the iPhone 5.
Philippe J DEWOST's insight:
40x in just 6 years !
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