Paper on ML-Enabled Geometric Compliance Improvement for Two-Photon Lithography Published in JMP

Our paper titled “Machine-learning-enabled geometric compliance improvement in two-photon lithography without hardware modifications” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yuhang Yang and Prof. Chenhui Shao.

In recent years, two-photon lithography (TPL) has emerged as a practical and promising micro- and nano-fabrication technique for a wide range of applications. However, the geometric accuracy of TPL-fabricated 3D structures has not been well understood. This study reveals for the first time that systematic geometric errors exist in TPL-fabricated structures and such errors exhibit a strong spatial correlation. A general machine-learning-based framework is presented to quantitatively model and improve the geometric compliance in TPL. Two experimental case studies, one at the microscale and the other at the nanoscale, demonstrate that the proposed framework can effectively improve the geometric compliance without introducing any modifications to the hardware or process parameters, thereby facilitating more widespread adoption.

Read the full paper here.