1. Self-Aligned Double Patterning Decomposition for Overlay Minimization and Hot Spot Detection H. Zhang, Y. Du, M. D.F. Wong, R. Topaloglu Dept. of ECE, University of Illinois at Urbana-Champaign DAC 2011

2. Outline Introduction Overview of 2D SADP Process Layout Decomposition Problem Formulation Problem Reduction Experimental Results Conclusions

3. Introduction Double patterning lithography (DPL) is the enabling technology for printing in sub-32nm nodes DPL technologies can be classified into two major types:  Double-exposure double-patterning (DEDP)  Single-exposure double-patterning (SEDP) Self-aligned double patterning (SADP)

4. Introduction In SADP process, given a target layout, before the manufacturing, core mask and trim mask must be generated from the intended target layout. Unlike the DEDP, the core mask and trim mask are not always directly from the original layout. The problem of generating the core and trim mask from a 2D designed layout is called SADP decomposition.

5. Introduction

6. Overview of 2D SADP Process

7. Layout Decomposition Problem Formulation Feature Generation and ILP formulation

8. Layout Decomposition Problem Formulation Feature and non-feature region constraint:  Feature is true if and only if this location has trim mask and no side-wall.  Non-feature region:

9. Layout Decomposition Problem Formulation Core and trim mask geometry constraints: Sidewall adjacency rule:  S i is 1 if and only if C i is 0, among the core variables{C j, C j+1,…,C j+m } within the sidewall width distance, there is at least one variable equal to 1.

10. Layout Decomposition Problem Formulation Minimum corner-corner rule:  If C i is 1, C i+1 is 0 and C i+2 is 0, then all variables {C k, C k+1, …, C k+q } within the minimum corner-corner distance should be 0.

11. Layout Decomposition Problem Formulation Minimum space rule:  If C i is 1 and C i+1 is 0, then all variables {C i+2, C i+3, …, C i+p } within the minimum space distance should be 0.

12. Layout Decomposition Problem Formulation Minimum width rule:  If C i is 0 and C i+1 is 1, then all variables {C i+2, C i+3, …, C i+n } within the minimum width distance should be 1.

13. Layout Decomposition Problem Formulation Objective for Overlay Minimization  The most critical target for the layout decomposition is to minimize the total overlay, in other words, to maximize the non-overlay boundaries’ length.  Non-overlay boundary should be guarded by sidewalls, and the trim mask will overlap with the sidewalls for at least length W 0.  The summation of all the trim variables within distance W 0 of any feature to be B.

14. Layout Decomposition Problem Formulation Decomposability Check and Hotspot Detection  By finding the minimum conflicting constraint set, we can perform hot spot detection.  Inserting extra binary slack variables onto each constraint.  Minimize the summation of the slack variable will be equal to finding the minimum number of conflicting constraints.

15. Problem Reduction Feature Region Variable Reduction  In one single feature, the trim variable will always be 1, and the sidewall variable will be 0.  Combine core variables in one continuous feature.

16. Problem Reduction Core and Sidewall Variable Reduction

17. Problem Reduction Core and Sidewall Variable Reduction  Only need to assign variables on the regions which are within distance 2W s +W c,min.

18. Problem Reduction Trim Variable Reduction

19. Experimental Results

22. Conclusions This paper has finished the SADP decomposition process with overlay minimization and hot spot detection. For a decomposable layout, this algorithm guarantees to find a decomposable solution that minimizes overlay.