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Huawei unveils new approach to strengthen dominance in high-end chips

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Shanghai | May 25, 2026 1:24:23 PM IST
Chinese tech giant Huawei has unveiled a new approach that will enable it to manufacture high-end chips that could rival some of the most advanced chips from its rivals, like Intel and TSMC.

Huawei claims the new advanced chips that it has planned to make are based on the Tau Scaling Law that could achieve transistor density levels equivalent to 1.4 nm.

The company said the approach will allow it to make advanced chips without relying on the machines that some of its rivals use and that are barred for the Chinese manufacturer owing to US sanctions.

The move could make it difficult for Nvidia to make a comeback in the Chinese market, even as its chief, Jensen Huang, is hopeful that a thaw would let the company sell its advanced chips in China.

Last week, the Nvidia CEO told CNBC the U.S. chipmaker had "conceded" the Chinese market to Huawei.

The workaround that the Chinese tech behemoth has come up with will let it make chips that match the transistor density of those manufactured with a 1.4-nanometre process.

This will be a huge shift in the US-China dynamics as Huawei's success in this new frontier for the world's semiconductor players will make it more independent, establishing its prowess in cutting-edge technology.

Washington has clamped down on Beijing with sanctions that restrict access to high-end lithography tools and other technologies needed to make the next generation of chips.

Taiwan's TSMC currently uses tech that makes 2 nm chips and plans to roll out 1.4 nm tech in a few years. Others like Intel and Samsung are also working to mass-produce the 1.4 nm chips in the next couple of years.

Huawei revealed that the Kirin chips scheduled for launch later this year will be made with what it called "LogicFolding" architecture that will enhance chip performance.

The company said its approach involves stacking multiple layers of circuits in a single chip that will allow faster movement of data within them and improve computing efficiency. (ANI)

 
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