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The rise in popularity of two-photon polymerization (TPP) as an additive manufacturing technique has impacted many areas of science and engineering, particularly those related to biomedical applications. Compared with other fabrication methods used for biomedical applications, TPP offers 3D, nanometer-scale fabrication dexterity (free-form). Moreover, the existence of turnkey commercial systems has increased accessibility. In this review, we discuss the diversity of biomedical applications that have benefited from the unique features of TPP. We also present the state of the art in approaches for patterning and reading 3D TPP-fabricated structures. The reading process influences the fidelity for both in situ and ex situ characterization methods. We also review efforts to leverage machine learning to facilitate process control for TPP. Finally, we conclude with a discussion of both the current challenges and exciting opportunities for biomedical applications that lie ahead for this intriguing and emerging technology. 

Two-photon polymerization (TPP) is an advanced 3D fabrication technique capable of creating features with submicron precision. A primary challenge in TPP lies in the facile and accurate characterization of fabrication quality, particularly for structures possessing complex internal features. In this study, we introduce an automated brightfield layerwise evaluation technique that enables a simple-to-implement approach forin situmonitoring and quality assessment of TPP-fabricated structures. Our approach relies on sequentially acquired brightfield images during the TPP writing process and using background subtraction and image processing to extract layered spatial features. We experimentally validate our method by printing a fibrous tissue scaffold and successfully achieve an overall system-adjusted fidelity of 87.5%in situ. Our method is readily adaptable in most TPP systems and can potentially facilitate high-quality TPP manufacturing of sophisticated microstructures.