Investigation of machine learning for dual OPC and assist feature printing optimization

Marco Guajardo; Yulu Chen; Peter Brooker; C.-E. Wu; Kevin Hooker; Folarin Latinwo; Kevin Lucas

doi:10.1117/12.2516134

20 March 2019 Investigation of machine learning for dual OPC and assist feature printing optimization

Marco Guajardo, Yulu Chen, Peter Brooker, C.-E. Wu, Kevin Hooker, Folarin Latinwo, Kevin Lucas

Author Affiliations +

Proceedings Volume 10962, Design-Process-Technology Co-optimization for Manufacturability XIII; 109620E (2019) https://doi.org/10.1117/12.2516134
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

As feature resolution and process variations continue to shrink for new nodes of both DUV and EUV lithography, the density and number of devices on advanced semiconductor masks continue to increase rapidly. These advances cause significantly increased pressure on the accuracy and efficiency of OPC and assist feature (AF) optimization methods for each subsequent process technology. Several publications and industry presentations have discussed the use of neural networks or other machine learning (or even deep learning) to provide improvements in efficiency for OPC main feature optimization or AF placement. However, these two mask synthesis steps are not independent. OPC affects AF optimum position and size; and AF position and size both affect the final optimum OPC main feature correction. A challenging example of these interactions is the need for OPC and AF methods to be aware of potential AF wafer printing. AF printing on the wafer can lead to catastrophic device failure. If an AF is at risk of printing in photoresist, both the OPC and the size (and potentially the position) of the AF need to be modified accurately and efficiently. Recent advancements in lithography utilizing negative tone develop (NTD) photoresists (resists) with strong physical shrink effects also further increase the difficulty of accurately modeling AF printing. In this paper, we present results of our work to explore the requirements, the issues and the overall potential for developing robust, accurate and fast integrated machine learning methods to optimize OPC and AFs.

Citation Download Citation

Marco Guajardo, Yulu Chen, Peter Brooker, C.-E. Wu, Kevin Hooker, Folarin Latinwo, and Kevin Lucas "Investigation of machine learning for dual OPC and assist feature printing optimization", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620E (20 March 2019); https://doi.org/10.1117/12.2516134

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KEYWORDS

Printing

Optical proximity correction

3D modeling

Calibration

Photomasks

Semiconducting wafers

Data modeling

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