Evaluation of Lithographic Benefits of using ILT Techniques for 22nm-node Yi Zou*, Yunfei Deng, Jongwook Kye, Luigi Capodieci and Cyrus Tabery Globalfoundries, 1050 E. Arques Ave. Sunnyvale, CA. 94085 Thuc Dam, Anthony Aadamov, Ki-Ho Baik, Linyong Pang, and Bob Gleason Luminescent Technologies, 2471 East Bayshore Suite 600, Palo Alto, CA 94303 *Contact: yi.zou@globalfoundries.com Tel: (408) 749-3455 ABSTRACT As increasing complexity of design and scaling continue to push lithographic imaging to its k1 limit, lithographers have been developing computational lithography solutions to extend 193nm immersion lithography to the 22nm technology node. In our paper, we investigate the beneficial source or mask solutions with respect to pattern fidelity and process variation (PV) band performances for 1D through pitch patterns, SRAM and Random Logic Standard Cells. The performances of two different computational lithography solutions, idealized un-constrained ILT mask and manhattanized mask rule constrain (MRC) compliant mask, are compared. Additionally performance benefits for process-window aware hybrid assist feature (AF) are gauged against traditional rule-based AF. The results of this study will demonstrate the lithographic performance contribution that can be obtained from these mask optimization techniques in addition to what source optimization can achieve. Keywords: assist feature (AF), computational lithography, Inverse Lithography Technologies (ILT), mask optimization (MO), source optimization (SO), Source Mask Optimization (SMO) 1. Introduction Source Optimization (SO) and Mask Optimization (MO) have been heavily investigated in lithography over the past decade. Especially, optimization of source and mask together (SMO) has become a key enabling technology for 22nm technology node to extend the life cycle of ArF single exposure lithography [1-2]. The improvement of patterning for SRAM and random logic clips has been demonstrated via advanced source fabrication technology and fully programmable illumination control at high pupil resolution. While SMO has become essential enabling technology for lithography processes at 22nm and below, the problem of how to implement SMO in pre-production and production flow (such as feasibility of using source and mask co- optimization for 22nm full-chip) is still open. Although we know the answer to “when more and more patterns are included in the optimization, will the optimal source always converge to the same solution?” is “No” [3]. Is that practically possible to throw a full logic chip into any SMO software? The current commonly implemented flow to use commercial SMO tool is to perform co-optimization for a limited number of patterns (mainly critical SRAM, tightest pitch, etc.) Critical measurement topologies, such as line-ends, edges and etc, need to be tagged and assigned to different weights according to their importance. The objective of printing those patterns can be defined in many different forms but process variations information (i.e. focus variation, dose variation, and mask error) must be included. Construction of PV-band is a technique to express process parameter variations such as dose, focus, and mask bias. PV-band width is sufficient as a quantitative Optical Microlithography XXIII, edited by Mircea V. Dusa, Will Conley, Proc. of SPIE Vol. 7640, 76400L · © 2010 SPIE · CCC code: 0277-786X/10/$18 · doi: 10.1117/12.848479 Proc. of SPIE Vol. 7640 76400L-1 Downloaded From: http://spiedigitallibrary.org/ on 08/13/2014 Terms of Use: http://spiedl.org/terms