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Lawrence Livermore National Laboratory scientists and collaborators are using machine learning to address two key barriers to industrialization of two-photon lithography (TPL): monitoring of part quality during printing and determining the right light dosage for a given material. The team developed a machine learning algorithm trained on thousands of video images of TPL builds to identify the optimal parameters for settings such as exposure and laser intensity and to automatically detect part quality at high accuracy.
Two-photon lithography (TPL)—a widely used 3-D nanoprinting technique that uses laser light to create 3-D objects—has shown promise in research applications but has yet to achieve widespread industry acceptance due to limitations on large-scale part production and time-in...
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