A random generation approach to pattern library creation for full chip lithographic simulation

Elain Zou; Sid Hong; Limei Liu; Lucas Huang; Legender Yang; Aliaa Kabeel; Kareem Madkour; Wael ElManhawy; Joe Kwan; Chunshan Du; Xinyi Hu; Qijian Wan; Recoo Zhang

doi:10.1117/12.2258133

4 April 2017 A random generation approach to pattern library creation for full chip lithographic simulation

Elain Zou, Sid Hong, Limei Liu, Lucas Huang, Legender Yang, Aliaa Kabeel, Kareem Madkour, Wael ElManhawy, Joe Kwan, Chunshan Du, Xinyi Hu, Qijian Wan, Recoo Zhang

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Proceedings Volume 10148, Design-Process-Technology Co-optimization for Manufacturability XI; 1014811 (2017) https://doi.org/10.1117/12.2258133
Event: SPIE Advanced Lithography, 2017, San Jose, California, United States

Abstract

As technology advances, the need for running lithographic (litho) checking for early detection of hotspots before tapeout has become essential. This process is important at all levels—from designing standard cells and small blocks to large intellectual property (IP) and full chip layouts. Litho simulation provides high accuracy for detecting printability issues due to problematic geometries, but it has the disadvantage of slow performance on large designs and blocks [1]. Foundries have found a good compromise solution for running litho simulation on full chips by filtering out potential candidate hotspot patterns using pattern matching (PM), and then performing simulation on the matched locations. The challenge has always been how to easily create a PM library of candidate patterns that provides both comprehensive coverage for litho problems and fast runtime performance. This paper presents a new strategy for generating candidate real design patterns through a random generation approach using a layout schema generator (LSG) utility. The output patterns from the LSG are simulated, and then classified by a scoring mechanism that categorizes patterns according to the severity of the hotspots, probability of their presence in the design, and the likelihood of the pattern causing a hotspot. The scoring output helps to filter out the yield problematic patterns that should be removed from any standard cell design, and also to define potential problematic patterns that must be simulated within a bigger context to decide whether or not they represent an actual hotspot. This flow is demonstrated on SMIC 14nm technology, creating a candidate hotspot pattern library that can be used in full chip simulation with very high coverage and robust performance.

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Elain Zou, Sid Hong, Limei Liu, Lucas Huang, Legender Yang, Aliaa Kabeel, Kareem Madkour, Wael ElManhawy, Joe Kwan, Chunshan Du, Xinyi Hu, Qijian Wan, and Recoo Zhang "A random generation approach to pattern library creation for full chip lithographic simulation", Proc. SPIE 10148, Design-Process-Technology Co-optimization for Manufacturability XI, 1014811 (4 April 2017); https://doi.org/10.1117/12.2258133

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KEYWORDS

Phase modulation

Lithography

Standards development

Image classification

Visualization

Intellectual property

Monte Carlo methods

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