Journal of Microelectronic Manufacturing (Mar 2020)

A Study of 2D Assist Feature Placement

  • Liang Zhu,
  • Barry Ma,
  • Lin Shen,
  • Kevin Beaudette

DOI
https://doi.org/10.33079/jomm.20030104
Journal volume & issue
Vol. 3, no. 1
pp. 1 – 4

Abstract

Read online

Sub-resolution assist features have been widely recognized in lithography patterning. In general, the insertion of assist features in optically adjacent space around main designed features, will change the aerial image intensity profiles of corresponding main features. Optimizing assist feature placement lets the main feature obtain optimal or better image contrast, better imaging resolution and depth of focus (DOF). Recent EUV lithography development, however, imposes strict budget of edge placement error and process window control causing assist features to become more and more complex. In this domain, 1D assisting feature can no longer meet such tight requirements, and 2D assisting features have become necessary in the semiconductor industry. In this paper, the process window and edge placement error evaluations of different 2D assist feature types are reviewed, along with their associated run time and memory consumption. Various types of 2D assist features are evaluated, including 45-degree disconnected assist features, 45-degree connected assisting features, Manhattan only assist feature arrays, and so on. To generate the assist features, the model-based assisting feature rule table is first generated using the optical model as the reference. The rule table is then split into different rule sets by considering the dimensions and types of assisting features. Finally, the CD variations across process window are evaluated as the success criteria of each assist feature rule sets. In addition, an inverse lithography technology (ILT) based approach is proposed to generate the optimized rule table, as ILT is well known to have considerable benefits in finding the best pattern solutions to improve process window, 2D CD control, and resolution in the low K1 lithography regime. At the end of this paper, the summary discusses how the assisting feature placement can be further optimized using leading-edge technologies like machine learning.

Keywords