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Current metal additive manufacturing (AM) systems suffer from limitations on the minimum feature sizes they can produce during part formation. The microscale selective laser sintering (μ-SLS) system addresses this drawback by enabling the production of parts with minimum feature resolutions of the order of a single micrometer. However, the production of microscale parts is challenging due to unwanted heat conduction within the nanoparticle powder bed. As a result, finite element (FE) thermal models have been developed to predict the evolution of temperature within the particle bed during laser sintering. These thermal models are not only computationally expensive but also must be integrated into an iterative model-based control framework to optimize the digital mask used to control the distribution of laser power. These limitations necessitate the development of a machine learning (ML) surrogate model to quickly and accurately predict the temperature evolution within the μ-SLS particle bed using minimal training data. The regression model presented in this work uses an “Element-by-Element” approach, where models are trained on individual finite elements to learn the relationship between thermal conditions experienced by each element at a given time-step and the element's temperature at the next time-step. An existing bed-scale FE thermal model of the μ-SLS system is used to generate element-by-element tabular training data for the ML model. A data-efficient artificial neural network (NN) is then trained to predict the temperature evolution of a 2D powder-bed over a 2 s sintering window with high accuracy. 

Abstract High-throughput and cost-efficient fabrication of intricate nanopatterns using top-down approaches remains a significant challenge. To overcome this limitation, advancements are required across various domains: patterning techniques, real-time and post-process metrology, data analysis, and, crucially, process control. We review recent progress in continuous, top-down nanomanufacturing, with a particular focus on data-driven process control strategies. We explore existing Machine Learning (ML)-based approaches for implementing key aspects of continuous process control, encompassing high-speed metrology balancing speed and resolution, modeling relationships between process parameters and yield, multimodal data fusion for comprehensive process monitoring, and control law development for real-time process adjustments. To assess the applicability of established control strategies in continuous settings, we compare roll-to-roll (R2R) manufacturing, a paradigmatic continuous multistage process, with the well-established batch-based semiconductor manufacturing. Finally, we outline promising future research directions for achieving high-quality, cost-effective, top-down nanomanufacturing and particularly R2R nanomanufacturing at scale.