Printability Prediction in Projection Two-Photon Lithography Via Machine Learning Based Surrogate Modeling of Photopolymerization
Printability Prediction in Projection Two-Photon Lithography Via Machine Learning Based Surrogate Modeling of Photopolymerization
复制标题
通过基于机器学习的光聚合代理模型预测投影双光子光刻的适印性
DOI：
10.1115/1.4063021
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Saha, Sourabh K.
中科院分区：
文献类型：
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作者：
Pingali, Rushil;Saha, Sourabh K.
双光子光刻 (TPL) 是一种直接激光写入工艺，能够制造具有亚微米分辨率的厘米级复杂三维聚合物结构。与传统 TPL 的缓慢串行写入方案相比，投影 TPL (P-TPL) 可以一次快速打印整个层。然而，由于缺乏计算高效的模型，过程预测仍然是 P-TPL 中的一个重大挑战。在这项工作中，我们提出了基于机器学习的替代模型来预测 P-TPL 的结果，其准确度达到基于物理的反应扩散有限元模拟的 98% 以上。使用基于物理的模拟生成的数据来训练分类神经网络。这使我们能够在计算上高效且准确地预测一组打印条件是否会导致精确且可控的聚合，以及所需的打印与无打印或失控聚合的比较。我们询问这个替代模型来研究有希望成功打印的参数机制。我们预测在给定的一组处理条件下打印所需的光刻胶反应速率常数的组合，从而生成一组适印性图。替代模型将生成这些地图所需的计算时间从 10 多个个月减少到不到一秒。因此，这些模型可以在过程控制和优化期间快速、明智地选择光刻胶和印刷参数。
Two-photon lithography (TPL) is a direct laser writing process that enables the fabrication of cm-scale complex three-dimensional polymeric structures with submicrometer resolution. In contrast to the slow and serial writing scheme of conventional TPL, projection TPL (P-TPL) enables rapid printing of entire layers at once. However, process prediction remains a significant challenge in P-TPL due to the lack of computationally efficient models. In this work, we present machine learning-based surrogate models to predict the outcomes of P-TPL to> 98% of the accuracy of a physics-based reaction-diffusion finite element simulation. A classification neural network was trained using data generated from the physics-based simulations. This enabled us to achieve computationally efficient and accurate prediction of whether a set of printing conditions will result in precise and controllable polymerization and the desired printing versus no printing or runaway polymerization. We interrogate this surrogate model to investigate the parameter regimes that are promising for successful printing. We predict combinations of photoresist reaction rate constants that are necessary to print for a given set of processing conditions, thereby generating a set of printability maps. The surrogate models reduced the computational time that is required to generate these maps from more than 10 months to less than a second. Thus, these models can enable rapid and informed selection of photoresists and printing parameters during process control and optimization.