BRITE Pivot: Machine Learning Enabled Rapid and Robust Three-Dimensional Nanomanufacturing

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Three-dimensional (3D) printing is one of the most important manufacturing technology developments in recent years for applications ranging from prototyping and product visualization to building functional materials and devices. Nanoscale 3D printing has been used to produce a wide range of complex 3D nanostructures and nanodevices with unprecedented properties and functionalities. However, the slow speed, variable quality and poor reproducibility of current 3D nanoprinting methods, such as point-by-point laser printing, are barriers for their adoption to commercial-scale manufacturing. Machine learning (ML) and artificial intelligence (AI) are ideally suited for improving and assuring the quality of the printed structures. This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot award develops AI-guided, ML-enabled 3D nanoprinting methods to improve the speed, scale, print quality and robustness of the printed structures and devices. These AI and ML tools for 3D nanomanufacturing enable new applications, such as sensors and wearables, that benefit several sectors of the economy and contribute to US competitiveness and global leadership in advanced nanomanufacturing. This project contributes to diversity, equity and inclusion by recruiting students from underrepresented groups and engaging them in research and education in AI, ML and 3D nanoprinting technologies, thus developing a diverse workforce in advanced manufacturing. The PI has extensive experience and expertise in laser-based nanomanufacturing and device manufacture. Recent efforts by the principal investigator (PI)’s group have resulted in the development of a rapid, continuous, layer-by-layer 3D nanoprinting technology. This project allows the PI to acquire new expertise in ML and AI methods through in-depth investigations and applications of these methods to develop rapid and robust 3D nanoprinting technologies. This includes studies and evaluations of various ML and AI methods and selection and implementation of appropriate methods for rapid 3D nanoprinting. The research involves developing methods that combine physics-based models with advanced ML algorithms such as adaptive learning and transfer learning. The femtosecond laser-based 3D nanoprinting process involves understanding the fundamentals of controlling the laser beam characteristics, such as beam size and scan rate, its propagation in the optical system and the laser-induced reactions in the photopolymers. The physics-based models and laser photo-polymerization experiments provide key data and guidance for the development of ML and AI analytic tools for process control and optimization. Furthermore, the project studies approaches to develop ML and AI tools using a relatively small amount of experimental data. Using results of various printed structures, ML and AL algorithms are developed to guide the process of printing arbitrary, user-defined structures with high precision. This project advances knowledge in AI-guided, ML-enabled femtosecond laser-based photo-polymerization as a platform for rapid and robust 3D nanomanufacturing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.