PSCAR optimization to reduce EUV resist roughness with sensitization using Resist Formulation Optimizer (RFO)

Seiji Nagahara; Cong Que Dinh; Gosuke Shiraishi; Yuya Kamei; Kathleen Nafus; Yoshihiro Kondo; Michael Carcasi; Yukie Minekawa; Hiroyuki Ide; Yuichi Yoshida; Kosuke Yoshihara; Ryo Shimada; Masaru Tomono; Kazuhiro Takeshita; Serge Biesemans; Hideo Nakashima; Danilo De Simone; John S. Petersen; Philippe Foubert; Peter De Bisschop; Geert Vandenberghe; Hans-Jürgen Stock; Balint Meliorisz

doi:10.1117/12.2515187

8 April 2019 PSCAR optimization to reduce EUV resist roughness with sensitization using Resist Formulation Optimizer (RFO)

Seiji Nagahara, Cong Que Dinh, Gosuke Shiraishi, Yuya Kamei, Kathleen Nafus, Yoshihiro Kondo, Michael Carcasi, Yukie Minekawa, Hiroyuki Ide, Yuichi Yoshida, Kosuke Yoshihara, Ryo Shimada, Masaru Tomono, Kazuhiro Takeshita, Serge Biesemans, Hideo Nakashima, Danilo De Simone, John S. Petersen, Philippe Foubert, Peter De Bisschop, Geert Vandenberghe, Hans-Jürgen Stock, Balint Meliorisz

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Proceedings Volume 10960, Advances in Patterning Materials and Processes XXXVI; 109600A (2019) https://doi.org/10.1117/12.2515187
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

Resist Formulation Optimizer (RFO) is created to optimize resist formulation under EUV stochastic effects. Photosensitized Chemically Amplified Resist^TM (PSCARTM) 2.0 reaction steps are included in the resist reaction model in RFO in addition to standard Chemically Amplified Resists (CAR) reaction steps. A simplified resist roughness calculation method is introduced in RFO. RFO uses “fast stochastic resist model” which uses continuous model information for stochastic calculation. “Resist component’s dissolution inhibition model” is also introduced for better prediction of different resist formulations in RFO. The resist component’s dissolution inhibition model is used for calculation of both Dissolution Inhibition Slope (DIS) and Dissolution Inhibition Deviation (DID). By dividing DID by DIS at a pattern edge, Line Edge Roughness (LER) can be predicted. The RFO performance is validated to give low residual errors after calibration even for different resist formulations. RFO is designed to optimize the resist formulation to minimize resist roughness as a cost function with keeping target CD. RFO suggests that PSCAR 2.0 with Polarity Switching photosensitizer precursor (POLAS) in combination with photosensitizer (PS) image enhancement may provide reduced resist roughness. Simulations using a calibrated rigorous stochastic resist model for S-Litho show a good prediction of PSCAR 2.0 process performance.

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Seiji Nagahara, Cong Que Dinh, Gosuke Shiraishi, Yuya Kamei, Kathleen Nafus, Yoshihiro Kondo, Michael Carcasi, Yukie Minekawa, Hiroyuki Ide, Yuichi Yoshida, Kosuke Yoshihara, Ryo Shimada, Masaru Tomono, Kazuhiro Takeshita, Serge Biesemans, Hideo Nakashima, Danilo De Simone, John S. Petersen, Philippe Foubert, Peter De Bisschop, Geert Vandenberghe, Hans-Jürgen Stock, and Balint Meliorisz "PSCAR optimization to reduce EUV resist roughness with sensitization using Resist Formulation Optimizer (RFO)", Proc. SPIE 10960, Advances in Patterning Materials and Processes XXXVI, 109600A (8 April 2019); https://doi.org/10.1117/12.2515187

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KEYWORDS

Stochastic processes

Calibration

Extreme ultraviolet

Line edge roughness

3D modeling

Extreme ultraviolet lithography

Switching

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