ASML to Launch Universal EUV Lithography Platform

TapTechNews May 23rd news, according to Dutch media Bits&Chips, Martin van den Brink, an ASML advisor and former CTO, recently said that this lithography machine manufacturer is considering launching a universal EUV lithography platform.

Van den Brink stated at the 2024 imec ITF World Technology Forum held on the 21st to 22nd of this month:

We have proposed a long-term (perhaps ten years) roadmap: We will have a single platform that includes Low-NA (0.33 NA), High-NA (0.55 NA), and Hyper-NA (expected to be above 0.7 NA) EUV systems.

According to the Rayleigh criterion formula, a higher numerical aperture means better lithography resolution.

Van den Brink said that the future Hyper-NA lithography machine will simplify the advanced process production process and avoid the additional steps and risks brought by double patterning with the High-NA lithography machine to achieve the same accuracy.

Multiple EUV lithography machines share a basic platform, which not only reduces the development cost but also facilitates the backward transplantation of the technical improvements of the Hyper-NA machine to the lithography machine with a lower numerical aperture.

According to TapTechNews' previous report, ASML's latest 0.33 NA EUV lithography machine - NXE:3800E has imported the rapid stage movement system developed for the High-NA lithography machine.

ASML to Launch Universal EUV Lithography Platform_0

In addition, ASML also plans to increase the wafer throughput of its DUV and EUV lithography machines from the current 200 to 300 wafers per hour to 400 to 500 wafers per hour, thereby improving the production efficiency of a single lithography machine and reducing the industry cost from another aspect.

In the speech, Van den Brink also mentioned: 'The current development trend of artificial intelligence shows that consumers have a strong demand for a variety of applications. And the factors limiting the demand include energy consumption, computing power, and the required massive data sets.'

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