SUMMARY

Two-photon lithography (TPL) is a direct laser writing technique that is capable of printing cm-scale three-dimensional structures with submicron-scale features. Projection two-photon lithography (P-TPL) is a variant of TPL that is a thousand times faster than conventional point-by-point processing techniques. P-TPL achieves faster printing by enabling the projection and printing of entire layers at once. However, its practical utility is limited by the lack of accurate process models. P-TPL is challenging to computationally model because the timescales vary from femtoseconds to seconds and the length scales vary from tens of nanometers to hundreds of micrometers. In this work, the first physics-based computational models of the photopolymerization processes underlying P-TPL are developed. The resulting reaction-diffusion model is capable of accurately predicting feature sizes under various printing conditions. A surrogate machine learning model is trained on data generated from the reaction-diffusion simulations and is shown to be capable of rapidly predicting printability across large parameter spaces. These simulation capabilities are used to characterize the performance and operating limits of P-TPL. Specifically, defects due to chemical proximity effects arising from oxygen diffusion are studied and methods of minimizing them are devised. Additionally, rate and resolution limits are characterized, and strategies to maximize both objectives by printing with combinations of multiple projections are evaluated. Through the use of the surrogate models, the broader P-TPL parameter space is explored and a map of printable regimes is generated. These simulation capabilities represent an important step towards achieving a truly scalable nanoscale additive manufacturing technology.