| Autor: |
Hideo Nakashima, Yuichi Yoshida, John S. Petersen, Cong Que Dinh, Balint Meliorisz, Geert Vandenberghe, Philippe Foubert, Kosuke Yoshihara, Kathleen Nafus, Hans-Jürgen Stock, Masaru Tomono, Yuya Kamei, Seiji Nagahara, Michael A. Carcasi, Ryo Shimada, Yoshihiro Kondo, Danilo De Simone, Yukie Minekawa, Peter De Bisschop, Gosuke Shiraishi, Hiroyuki Ide, Serge Biesemans, Kazuhiro Takeshita |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Advances in Patterning Materials and Processes XXXVI. |
| Popis: |
Resist Formulation Optimizer (RFO) is created to optimize resist formulation under EUV stochastic effects. Photosensitized Chemically Amplified ResistTM (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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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