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Abstract Two-photon lithography (TPL) is a direct laser writing process that enables the fabrication of cm-scale complex three-dimensional polymeric structures with submicrometer resolution. In contrast to the slow and serial writing scheme of conventional TPL, projection TPL (P-TPL) enables rapid printing of entire layers at once. However, process prediction remains a significant challenge in P-TPL due to the lack of computationally efficient models. In this work, we present machine learning-based surrogate models to predict the outcomes of P-TPL to >98% of the accuracy of a physics-based reaction-diffusion finite element simulation. A classification neural network was trained using data generated from the physics-based simulations. This enabled us to achieve computationally efficient and accurate prediction of whether a set of printing conditions will result in precise and controllable polymerization and the desired printing versus no printing or runaway polymerization. We interrogate this surrogate model to investigate the parameter regimes that are promising for successful printing. We predict combinations of photoresist reaction rate constants that are necessary to print for a given set of processing conditions, thereby generating a set of printability maps. The surrogate models reduced the computational time that is required to generate these maps from more than 10 months to less than a second. Thus, these models can enable rapid and informed selection of photoresists and printing parameters during process control and optimization.
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In situ photografting during direct laser writing in thermoplastic microchannels
Abstract A method for in situ photografting during direct laser writing by two-photon polymerization is presented. The technique serves as a powerful approach to the formation of covalent bonds between 3D photoresist structures and thermoplastic surfaces. By leveraging the same laser for both pattern generation and localized surface reactions, crosslinking between the bulk photoresist and thermoplastic surface is achieved during polymerization. When applied to in-channel direct laser writing for microfluidic device fabrication, the process yields exceptionally strong adhesion and robust bond interfaces that can withstand pressure gradients as high as 7 MPa through proper channel design, photoinitiator selection, and processing conditions.
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- Award ID(s):
- 1950234
- PAR ID:
- 10231151
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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