By Ron Wilson

What’s at stake:
With 3nm processes barely in production, the industry is already talking about 2nm — and sliding the schedules. With a whole new kind of transistor and massive technical challenges, 2nm will be a heavy lift not only for the foundries, but for the EDA companies that have to support it and the customers who have to design for it.

Almost drowned out by the shouting about chiplets, the so-called 2nm process node — the next full step after 3nm — is moving toward production. It promised developers of CPUs, GPUs, AI chips and, eventually, smartphone application processors, a whole lot more transistors, a little less power consumption — if designers are very careful — and a lot more hard work.

But what is the reality? When is 2nm coming? How is it different from 3nm? And what has to happen to make 2nm a useable process for actual chip designers?

By Ron Wilson

What’s at stake:
Ideally, chiplets could be off-the-shelf products that snap together like building blocks — no EDA tools required. Anyone who could specify exactly what they wanted could create a system-in-package implementation, opening up silicon to a far wider range of designers, and, incidentally, undermining parts of the EDA and design-services industries. Could it ever really happen?

One early conception of chiplets — in their formative days within the US Department of Defense — was of an open market. You could buy the functions you needed off the shelf, arrange them into a multi-die assembly, and after verification and analysis have a finished hardware design. Later thinking took this idea ever further. What if you could, figuratively speaking, just snap the chiplets together like building blocks, with no complex design automation tools or analyses needed, and could be sure that the resulting assembly would work correctly?

By Ron Wilson

What’s at stake:
Arm’s public offering could be a chance to invest in the explosive growth of artificial intelligence. Or investors could be buying into a total misunderstanding of Arm and what it does — whether AI takes off or not.

No recent tech IPO has stirred as much discussion as Softbank’s plan to float about ten percent of Arm Ltd. on the US NASDAQ exchange. Although Softbank has quietly backed off from their initial suggestions about an offering price, the figures still imply earnings growth close to that of Nvidia. That is, astronomical.

By Ron Wilson

What’s at stake:
What lies in the future for chiplets? They could change the structure of the semiconductor industry, freeing it from the geriatric grip of Moore’s Law and the hegemony of three giant manufacturers. Or they could, like thin-film hybrids and multidie packages before them, withdraw into a few application niches where their challenges are manageable and their costs acceptable.

By Ron Wilson

What’s at stake?
Advanced semiconductor packaging are neither routine nor affordable. But if they were, the industry could experience the triggering of an avalanche of chiplets, allowing IP vendors to sell silicon, erase many of the advantages of rich design teams and disaggregate the IC supply chain.

Many dies in one package. This is getting to be the topic of the month in the industry. But there are several subjects interdigitated in this area, and they are rapidly getting tangled together. This article combs them out by examining three different but related topics: multiple dies in a package; advanced packaging and; chiplets.

By Ron Wilson

What’s at stake:
With all the claims and commentary about AI systems, you can’t tell the insightful from the frightful without a program. That means digging into the different things hiding under the AI umbrella.

Artificial Intelligence (AI) is everywhere. From medical research labs to your car, from police stations to your vacuum cleaner, there is no escaping it. This ubiquity begs for a good definition. But there is no simple definition — a myriad of different technologies huddle under the AI umbrella.

Unfortunately, the term itself can add perceived value to a product. So AI gets stretched beyond its natural bounds, to fit any situation where it might improve profit margins. But to understand what is really happening with AI today we need a more precise definition.

By David Benjamin

“Generating a bio is a great way to show people some of the limitations of this system. Asking it to generate a bio for the same person three times in a row is an eye-opener for some. For me it was wrong universities, wrong field of study, and made up some awards that don’t exist. But it did upgrade me to IEEE Fellow (nice!)”

— Prof. Philip Koopman, Carnegie-Mellon University

Phil Koopman devised a foolproof method (see above) for testing the data-farming accuracy of the latest “artificial intelligence” application, ChatGPT—which is supposed to be so good at writing that journalists, p.r. flacks, and even “creative types” like me will soon be tucked away in a bed of mothballs.