Computational reaction networks applied to reaction cascades in EUV lithography

Next-generation semiconductor manufacturing achieving sub-10nm feature sizes requires highly precise lithographic patterning with extreme ultraviolet (EUV) light. However, EUV exposure of polymeric photoresists drives a reaction cascade via photoionization and low-energy electron attachment which remains poorly understood, limiting spatial control and pattern resolution. Chemical reaction networks (CRNs) are powerful tools for obtaining insight into complex reactive processes. However, they are difficult to employ when reaction mechanisms and products are not thoroughly understood. Here we describe new methods of CRN generation and analysis that seek to overcome these limitations. We combine high-throughput density functional theory calculations, numerical analysis, and machine learning to tractably build up CRNs from only starting species, predicting possible cascade products and their formation pathways. We apply this methodology to study secondary-electron-driven reactivity in EUV lithography and present preliminary results.