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Basic approaches to simulation of resist mask formation in computational lithography

Nikita N. Balan 1
Nikita N. Balan
, 
Vladidmir V. Ivanov 1
Vladidmir V. Ivanov
, 
Alexey K. Kuzovkov 1
Alexey K. Kuzovkov
, 
Evgenia V. Sokolova 1
Evgenia V. Sokolova
, 
Evgeniy S. Shamin 1
Evgeniy S. Shamin
1 Molecular Electronics Research Institute (JSC MERI), 12/1 1st Zapadnyi Proezd, Zelenograd, Moscow, 124460, Russia
10.3897/j.moem.6.1.55056
Published 30 March 2020
Communication , Volume 6, Issue 1, 37-45
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Balan N. N. et al. Basic approaches to simulation of resist mask formation in computational lithography // Modern Electronic Materials. 2020. Vol. 6. No. 1. pp. 37-45.
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Balan N. N., Ivanov V. V., Kuzovkov A. K., Sokolova E. V., Shamin E. S. Basic approaches to simulation of resist mask formation in computational lithography // Modern Electronic Materials. 2020. Vol. 6. No. 1. pp. 37-45.
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TY - JOUR
DO - 10.3897/j.moem.6.1.55056
UR - https://moem.colab.ws/publications/10.3897/j.moem.6.1.55056
TI - Basic approaches to simulation of resist mask formation in computational lithography
T2 - Modern Electronic Materials
AU - Balan, Nikita N.
AU - Ivanov, Vladidmir V.
AU - Kuzovkov, Alexey K.
AU - Sokolova, Evgenia V.
AU - Shamin, Evgeniy S.
PY - 2020
DA - 2020/03/30
PB - НИТУ МИСИС
SP - 37-45
IS - 1
VL - 6
ER -
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@article{2020_Balan,
author = {Nikita N. Balan and Vladidmir V. Ivanov and Alexey K. Kuzovkov and Evgenia V. Sokolova and Evgeniy S. Shamin},
title = {Basic approaches to simulation of resist mask formation in computational lithography},
journal = {Modern Electronic Materials},
year = {2020},
volume = {6},
publisher = {НИТУ МИСИС},
month = {Mar},
url = {https://moem.colab.ws/publications/10.3897/j.moem.6.1.55056},
number = {1},
pages = {37--45},
doi = {10.3897/j.moem.6.1.55056}
}
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Balan, Nikita N., et al. “Basic approaches to simulation of resist mask formation in computational lithography.” Modern Electronic Materials, vol. 6, no. 1, Mar. 2020, pp. 37-45. https://moem.colab.ws/publications/10.3897/j.moem.6.1.55056.
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Keywords

k-means clustering method.
CM1
photoresist
projection photolithography
VT5

Abstract

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.

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