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Proceedings Paper

Mask technology for 0.25 um lithography and beyond
Author(s): Fu-Chang Lo
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Paper Abstract

Mask technology, for 0.25gm lithography and beyond, presents a significant challenge to both the mask industry and silicon industry. It is expected that optical lithography will continue to be the predominant approach, and the exposure wavelength of lithography tools will be pushed down from 365nm to 248nm and 193nm. It becomes inevitable that optical enhancements are required to provide necessary capabilities for achieving resolution at 0.2i.tm or below in manufacturing mode. However, most optical enhancement techniques such as phase shifting mask (PSM), optical proximity correction (OPC), off-axis illumination, and some combination of these methods, require substantial mask development efforts. The mask industry as a whole needs to deal with new modules, new materials, new pattern designs, sub-micron resolution, CD non- linearity effects, and high data volume, and then figure out a way to integrate solutions into silicon technology at a pace no slower than that required for silicon technology development. In many cases, a unique process or solution needs to be developed which is specific to one particular layer or one particular lithography option. In addition, in order to achieve high lithography productivity output, serious considerations need to be given for a possible increase in mask size, which by itself will have tremendous impact to the entire mask industry on tool set and material development. There is no doubt that mask technology has become a crucial and integral part of silicon technology. Its role is becoming increasingly important at 0.2511m lithography and beyond. In this presentation, we will first describe the role of mask technology by examining its significance to silicon technology. Secondly we will describe the major challenges facing mask technology. In the mask fabrication section, a few examples will be given to highlight major progress and key issues. Next, phase shifting mask technology and its challenges will be summarized. Lastly, a conclusion will be given.

Paper Details

Date Published: 30 August 2017
PDF: 27 pages
Proc. SPIE 10320, An Introduction to Biological and Artificial Neural Networks for Pattern Recognition, 1032006 (30 August 2017); doi: 10.1117/12.2284079
Show Author Affiliations
Fu-Chang Lo, Intel Corp. (United States)


Published in SPIE Proceedings Vol. 10320:
An Introduction to Biological and Artificial Neural Networks for Pattern Recognition
Steven K. Rogers; Matthew Kabrisky, Editor(s)

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