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Changing the Transistor Channel

Ending silicon’s central role in transistors could maintain the march of Moore’s Law

12 min read
Changing the Transistor Channel
Illustration: Harry Campbell

The transistor isn’t shrinking the way it used to. The best ones we have today are a patchwork of fixes and kludges: speed-boosting materials that push or pull on the silicon center, exotic insulators added to stanch leaks, and a new geometry that pops things out of the plane of the chip and into the third dimension. Now, to keep Moore’s Law going, chipmakers are eyeing another monumental change in transistor architecture.

This time, they’re taking aim at the current-carrying channels at the very heart of the device, replacing the silicon there with germanium and compound semiconductors known as III-Vs. If all goes well, these materials could usher in a new generation of speedier, less power-hungry transistors, allowing for denser, faster, cooler-running chips.

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New EV Prototype Leaves Range Anxiety in the Dust

Mercedes-Benz's Vision EQXX completed a record-breaking 747-mile run in May

5 min read
a silver car driving down the road with a mountain of switchbacks behind it

The Mercedes-Benz Vision EQXX

Mercedes-Benz

Not long ago, a 300-mile range seemed like a healthy target for electric cars. More recently, the 520-mile (837-kilometer) Lucid Air became the world’s longest-range EV. But that record may not stand for long.

The Mercedes-Benz Vision EQXX, and its showroom-bound tech, looks to banish range anxiety for good: In April, the sleek prototype sedan completed a 621-mile (1,000-kilometer) trek through the Alps from Mercedes’ Sindelfingen facility to the Côte d'Azur in Cassis, France with battery juice to spare. It built on that feat in late May, when the prototype covered a world-beating, bladder-busting 747 miles (1,202 kilometers) in a run from Germany to the Formula One circuit in Silverstone, U.K.

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Artificial Synapses 10,000x Faster Than Real Thing

New protonic programmable resistors may help speed learning in deep neural networks

3 min read
Conceptual illustration shows a brain shape made of circuits on a multilayered chip structure.
Ella Maru Studio and Murat Onen

New artificial versions of the neurons and synapses in the human brain are up to 1,000 times smaller than neurons and at least 10,000 times faster than biological synapses, a study now finds.

These new devices may help improve the speed at which the increasingly common and powerful artificial intelligence systems known as deep neural networks learn, researchers say.

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How New Storage Technologies Enhance HPC Systems

Different storage technologies can maximize the efficiency and effectiveness of HPC systems while providing high capacity and low latency storage, and minimizing network bandwidth and power consumption

1 min read
How New Storage Technologies Enhance HPC Systems

High-performance computing (HPC) has historically been available primarily to governments, research institutions, and a few very large corporations for modeling, simulation, and forecasting applications. As HPC platforms are being deployed in the cloud for shared services, high-performance computing is becoming much more accessible, and its use is benefiting organizations of all sizes. Increasing investment in the industrial internet of things (IIoT), artificial intelligence (AI), and electronic design automation (EDA) and silicon IP for engineering development are a few factors that are driving increased use of high-performance computing systems. In general, increasingly complex models for big data processing, simulation, and forecasting are driving a need for more compute power and greater storage capacity & performance.

This white paper highlights how different storage technologies can maximize the efficiency and effectiveness of HPC systems while providing high capacity and low latency storage, and minimizing network bandwidth and power consumption.