Hyper NA water immersion lithography at 193 nm and 248 nm

Author(s):  
Bruce W. Smith ◽  
Yongfa Fan ◽  
Jianming Zhou ◽  
Anatoly Bourov ◽  
Lena Zavyalova ◽  
...  
2004 ◽  
Author(s):  
Bruce W. Smith ◽  
Anatoly Bourov ◽  
Yongfa Fan ◽  
Lena V. Zavyalova ◽  
Neal V. Lafferty ◽  
...  

2007 ◽  
Author(s):  
Idriss Blakey ◽  
Lan Chen ◽  
Bronwin Dargaville ◽  
Heping Liu ◽  
Andrew Whittaker ◽  
...  

2020 ◽  
Vol 6 (1) ◽  
pp. 37-45
Author(s):  
Nikita N. Balan ◽  
Vladidmir V. Ivanov ◽  
Alexey V. Kuzovkov ◽  
Evgenia V. Sokolova ◽  
Evgeniy S. Shamin

Main currently used resist mask formation models and problems solved have been overviewed. Stages of "full physical simulation" have been briefly analyzed based on physicochemical principles for conventional diazonapthoquinone (DNQ) photoresists and chemically enhanced ones. We have considered the concepts of the main currently used compact models predicting resist mask contours for full-scale product topologies, i.e., VT5 (Variable Threshold 5) and CM1 (Compact Model 1). Computation examples have been provided for full and compact resist mask formation models. Full resist mask formation simulation has allowed us to optimize the lithographic stack for a new process. Optimal thickness ratios have been found for the binary anti-reflecting layers used in water immersion lithography. VT5 compact model calibration has allowed us to solve the problem of optimal calibration structure sampling for maximal coverage of optical image parameters space while employing the minimal number of structures. This problem has been solved using cluster analysis. Clustering has been implemented using the k-means method. The optimum sampling is 300 to 350 structures, the rms error being 1.4 nm which is slightly greater than the process noise for 100 nm structures. The use of SEM contours for VT5 model calibration allows us to reduce the rms error to 1.18 nm for 40 structures.


Author(s):  
Walter D. Gillespie ◽  
Toshihiko Ishihara ◽  
William N. Partlo ◽  
George X. Ferguson ◽  
Michael R. Simon

2004 ◽  
Author(s):  
J. Christopher Taylor ◽  
Charles R. Chambers ◽  
Ryan Deschner ◽  
Robert J. LeSuer ◽  
Willard E. Conley ◽  
...  
Keyword(s):  

Author(s):  
Bruce W. Smith ◽  
Anatoly Bourov ◽  
Jainming Zhou ◽  
Lena Zavyalova ◽  
Neal Lafferty ◽  
...  

2014 ◽  
Vol 219 ◽  
pp. 209-212 ◽  
Author(s):  
Lucile Broussous ◽  
D. Krejcirova ◽  
K. Courouble ◽  
S. Zoll ◽  
A. Iwasaki ◽  
...  

Titanium Nitride metal hard mask was first introduced for BEOL patterning at 65 nm [1] and 45 nm nodes [2]. Indeed, in this “Trench First Hard Mask” (TFHM) backend architecture, the dual hard mask stack (SiO2 & TiN) allows a minimized exposure of ULK materials to damaging plasma chemistries, both for line/via etch sequence, and lithography reworks operations. This integration scheme was successfully used for a BEOL pitch down to 90 nm for the 28 nm node, however, for the 14 nm technology node, 64 nm BEOL minimum pitch is required for the first metal levels. Because it is unable to resolve features below 80 nm pitch in a single exposure, conventional 193 nm immersion lithography must be associated with dual patterning schemes, so called Lithography-Etch-Lithography-Etch (LELE) patterning [3] for line levels and self-aligned via (SAV) process [4] for via patterning. In both cases, 2 lithography/etch/clean sequences are necessary to obtain one desired pattern, and associated reworks also become more challenging since first pattern is exposed to resist removal processes (plasma + wet clean). The reference wet cleans that were developed for 65 to 28 nm TiN hardmask patterning, utilizes commonly used chemistry for BEOL post-etch cleans, i.e. diluted hydrofluoric acid (dHF) followed by deionized water Nanospray (DIWNS) on 300 mm single wafer tool.


2005 ◽  
Author(s):  
Ralph R. Dammel ◽  
Georg Pawlowski ◽  
Andrew Romano ◽  
Frank M. Houlihan ◽  
Woo-Kyu Kim ◽  
...  
Keyword(s):  

2008 ◽  
Author(s):  
Kazuya Matsumoto ◽  
Elizabeth Costner ◽  
Isao Nishimura ◽  
Mitsuru Ueda ◽  
C. Grant Willson

2010 ◽  
Vol 110 (1) ◽  
pp. 321-360 ◽  
Author(s):  
Daniel P. Sanders
Keyword(s):  

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