Machine-learning-enabled geometric compliance improvement in two-photon lithography without hardware modifications
Machine-learning-enabled geometric compliance improvement in two-photon lithography without hardware modifications
复制标题
DOI：
10.1016/j.jmapro.2022.02.046
复制
发表时间：
2022-04
影响因子：
6.2
通讯作者：
Yuhang Yang;Varun A. Kelkar;Hemangg S. Rajput;Adriana C. Salazar Coariti;K. Toussaint;Chenhui Shao
中科院分区：
文献类型：
--
作者：
Yuhang Yang;Varun A. Kelkar;Hemangg S. Rajput;Adriana C. Salazar Coariti;K. Toussaint;Chenhui Shao
近年来，双光子光刻（TPL）作为一种实用的、具有广泛应用前景的微纳米制造技术而崭露头角。许多研究已经报告了改进TPL的过程控制和打印特征尺寸，包括结合一定程度的硬件改进，这可能对商业系统是禁止的。然而，tpl制造的三维结构的几何精度尚未得到很好的理解。在本研究中，提出了一个通用的基于机器学习的框架来定量建模和提高TPL的几何顺应性。该框架量化了装配式三维结构几何柔度的空间变化，进而设计了提高几何柔度的补偿策略。两个实验案例研究，一个在微观尺度和另一个在纳米尺度，提出了证明该框架的有效性。首次揭示了tpl结构存在系统几何误差，且几何误差具有很强的空间相关性。该补偿策略在微尺度和纳米尺度下，将关键几何特征的平均误差分别降低了79.7%和47.4%。实例研究表明，该框架可以在不改变硬件和工艺参数的情况下有效地改善几何顺应性，从而促进了更广泛的采用。
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. Numerous studies have reported improving the process control and printed feature size of TPL, including by incorporating some degree of hardware improvements, which may be prohibitive for commercial systems. However, the geometric accuracy of TPL-fabricated 3D structures has not been well understood. In this study, a general machine-learning-based framework is presented to quantitatively model and improve the geometric compliance in TPL. The framework quantifies the spatial variation in geometric compliance of fabricated 3D structures, and then designs compensation strategies to improve the geometric compliance. Two experimental case studies, one at the microscale and the other at the nanoscale, are presented to demonstrate the effectiveness of the framework. It is revealed for the first time that systematic geometric errors exist in TPL-fabricated structures and such errors exhibit a strong spatial correlation. The produced compensation strategies reduce the average errors in key geometric features at the microscale and nanoscale by up to 79.7% and 47.4%, respectively. The case studies 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.