Novel Mechanism-Based Descriptors for Extreme Ultraviolet-Induced Photoacid Generation: Key Factors Affecting Extreme Ultraviolet Sensitivity

Predicting photolithography performance in silico for a given materials combination is essential for developing better patterning processes. However, it is still an extremely daunting task because of the entangled chemistry with multiple reactions among many material components. Herein, we investigated the EUV-induced photochemical reaction mechanism of a model photoacid generator (PAG), triphenylsulfonium cation, using atomiC-Scale materials modeling to elucidate that the acid generation yield strongly depends on two main factors: the lowest unoccupied molecular orbital (LUMO) of PAG cation associated with the electron-trap efficiency 'before C-S bond dissociation' and the overall oxidation energy change of rearranged PAG associated with the proton-generation efficiency 'after C-S bond dissociation'. Furthermore, by considering stepwise reactions accordingly, we developed a two-parameter-based prediction model predicting the exposure dose of the resist, which outperformed the traditional LUMO-based prediction model. Our model suggests that one should not focus only on the LUMO energies but also on the energy change during the rearrangement process of the activated triphenylsulfonium (TPS) species. We also believe that the model is well suited for computational materials screening and/or inverse design of novel PAG materials with high lithographic performances.

Automated summary

In this study, atomiC-scale materials modeling was used to investigate the photochemical reaction mechanism of a model photoacid generator (PAG). The acid generation yield was found to depend strongly on the lowest unoccupied molecular orbital (LUMO) of the PAG cation and the overall oxidation energy change of rearranged PAG. A prediction model based on these factors outperformed traditional LUMO-based models, suggesting the importance of considering both LUMO energies and energy changes during rearrangement. The model is also suitable for computational materials screening and inverse design of novel PAG materials with high lithographic performances.