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

Area of Science:

• Materials Science
• Computational Chemistry
• Photolithography

Background:

• Predicting photolithography performance is crucial for developing advanced patterning processes but is challenging due to complex material interactions.
• Existing models often rely solely on the lowest unoccupied molecular orbital (LUMO) energy of photoacid generators (PAGs), which is insufficient for accurate performance prediction.

Purpose of the Study:

• To investigate the EUV-induced photochemical reaction mechanism of triphenylsulfonium cation, a model PAG.
• To develop a more accurate in silico prediction model for PAG performance in photolithography.

Main Methods:

• Atomistic-scale materials modeling was employed to study the EUV-induced photochemical reactions of triphenylsulfonium cation.
• A two-parameter prediction model was developed, incorporating LUMO energy and oxidation energy change during rearrangement.

Main Results:

• Acid generation yield is dependent on both the PAG cation's LUMO energy (electron-trap efficiency) and the oxidation energy change after rearrangement (proton-generation efficiency).
• The developed two-parameter model significantly outperformed traditional LUMO-based prediction models for resist exposure dose.

Conclusions:

• Accurate PAG performance prediction requires considering both electron-trapping and proton-generation efficiencies, not just LUMO energy.
• The new model facilitates computational screening and inverse design of novel PAG materials for enhanced lithographic performance.