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Proceedings Paper • Open Access

Accelerate lithography improvement for high performance computing
Author(s): John Y. Chen

Paper Abstract

Artificial intelligence (AI) with deep learning is taking off based on High Performance Computing (HPC) engines fueled by “Big Data” in the cloud. NVIDIA’s general-purpose GPU (Graphics Process Unit) is the ideal platform to accelerate computation with its inherent massive parallel processing capability. The Deep Learning machines for AI would be the new driver for the semiconductor industry. In the past, the minimum feature on a semiconductor chip has greatly shrunk with Moore’s law. From 1971 to 2018, as the feature size scaled from 10 μm to 10 nm, the transistors per chip increased from thousands to billions, and remarkably, its price has gone down to few % of a cent. However, going forward with Moore’s law discontinued in its scaling cadence, the economic benefit f scaling can hardly justify the increased cost of wafer manufacturing unless we can find a way to advance lithography and pack more transistors on a chip. In the near future, the only practical way is EUV including EUV mask, which has made great progress lately even though still challenges ahead. Illustrated by the latest and most complicated AI chip on this planet, the presenter will describe key lithographic requirements from an end user point of view. An example is given to show how precise the Edge Placement of a geometry needs to be controlled in order to scale IC density for the future technology nodes.

Paper Details

Date Published: 3 October 2018
PDF: 4 pages
Proc. SPIE 10809, International Conference on Extreme Ultraviolet Lithography 2018, 1080902 (3 October 2018); doi: 10.1117/12.2504658
Show Author Affiliations
John Y. Chen, NVIDIA Corp. (United States)

Published in SPIE Proceedings Vol. 10809:
International Conference on Extreme Ultraviolet Lithography 2018
Kurt G. Ronse; Eric Hendrickx; Patrick P. Naulleau; Paolo A. Gargini; Toshiro Itani, Editor(s)

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