What’s Different About Next-Gen Transistors


After nearly a decade and five major nodes, along with a slew of half-nodes, the semiconductor manufacturing industry will begin transitioning from finFETs to gate-all-around stacked nanosheet transistor architectures at the 3nm technology node. Relative to finFETs, nanosheet transistors deliver more drive current by increasing channel widths in the same circuit footprint. The gate-all-aroun... » read more

Foundational Changes In Chip Architectures


We take many things in the semiconductor world for granted, but what if some of the decisions made decades ago are no longer viable or optimal? We saw a small example with finFETs, where the planar transistor would no longer scale. Today we are facing several bigger disruptions that will have much larger ripple effects. Technology often progresses in a linear fashion. Each step provides incr... » read more

Why Silent Data Errors Are So Hard To Find


Cloud service providers have traced the source of silent data errors to defects in CPUs — as many as 1,000 parts per million — which produce faulty results only occasionally and under certain micro-architectural conditions. That makes them extremely hard to find. Silent data errors (SDEs) are random defects produced in manufacturing, not a design bug or software error. Those defects gene... » read more

EVs Raise Energy, Power, And Thermal IC Design Challenges


The transition to electric vehicles is putting pressure on power grids to produce more energy and on vehicles to use that energy much more efficiently, creating a gargantuan set of challenges that will affect every segment of the automotive world, the infrastructure that supports it, and the chips that are required to make all of this work. From a semiconductor standpoint, improvements in th... » read more

IC Architectures Shift As OEMs Narrow Their Focus


Diminishing returns from process scaling, coupled with pervasive connectedness and an exponential increase in data, are driving broad changes in how chips are designed, what they're expected to do, and how quickly they're supposed to do it. In the past, tradeoffs between performance, power, and cost were defined mostly by large OEMs within the confines of an industry-wide scaling roadmap. Ch... » read more

Toward Domain-Specific EDA


More companies appear to be creating custom EDA tools, but it is not clear if this trend is accelerating and what it means for the mainstream EDA industry. Whenever there is change, there is opportunity. Change can come from new abstractions, new options for optimization, or new limitations that are imposed on a tool or flow. For example, the slowing of Moore's Law means that sufficient prog... » read more

Designing For Thermal


Heat has emerged as a major concern for semiconductors in every form factor, from digital watches to data centers, and it is becoming more of a problem at advanced nodes and in advanced packages where that heat is especially difficult to dissipate. Temperatures at the base of finFETs and GAA FETs can differ from those at the top of the transistor structures. They also can vary depending on h... » read more

MicroLEDs Move Toward Commercialization


The market for MicroLED displays is heating up, fueled by a raft of innovations in design and manufacturing that can increase yield and reduce prices, making them competitive with LCD and OLED devices. MicroLED displays are brighter and higher contrast than their predecessors, and they are more efficient. Functional prototypes have been developed for watches, AR glasses, TVs, signage, and au... » read more

Big Changes In Architectures, Transistors, Materials


Chipmakers are gearing up for fundamental changes in architectures, materials, and basic structures like transistors and interconnects. The net result will be more process steps, increased complexity for each of those steps, and rising costs across the board. At the leading-edge, finFETs will run out of steam somewhere after the 3nm (30 angstrom) node. The three foundries still working at th... » read more

AI Power Consumption Exploding


Machine learning is on track to consume all the energy being supplied, a model that is costly, inefficient, and unsustainable. To a large extent, this is because the field is new, exciting, and rapidly growing. It is being designed to break new ground in terms of accuracy or capability. Today, that means bigger models and larger training sets, which require exponential increases in processin... » read more

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