Zhao Rongbo, Wang Xiaolin, Hu Ziyu, Xu Hong, He Xiangming
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China.
Nanoscale. 2023 Aug 25;15(33):13692-13698. doi: 10.1039/d3nr01356a.
The critical dimension (CD) of lithographic patterns is the most significant indicator for evaluating the imaging performance of photoresists, and its value is seriously affected by process conditions. However, the lithographic imaging system is highly nonlinear, and extensive exposure experiments are needed to obtain the desired CD. This consumes lots of time, manpower, and cost in screening for optimal process conditions. Here, we report a combined electron beam lithography (EBL) experiment and recurrent neural network (RNN) study on the CDs of metal oxide nanoparticle photoresists, and establish a CD RNN model. Leveraging the RNN model, a process condition filter is developed to screen suitable process conditions. The experimental results demonstrate that the prediction accuracy of the CD model exceeds 93%, and the photoresist patterns under the screened process conditions can satisfy the requirements of a preset CD. This work opens up a novel perspective for accurate EBL process modeling, and provides guidance for EBL experiments.
光刻图案的关键尺寸（CD）是评估光刻胶成像性能的最重要指标，其值会受到工艺条件的严重影响。然而，光刻成像系统具有高度非线性，需要进行大量曝光实验才能获得所需的关键尺寸。这在筛选最佳工艺条件时会消耗大量时间、人力和成本。在此，我们报告了一项关于金属氧化物纳米颗粒光刻胶关键尺寸的电子束光刻（EBL）实验与循环神经网络（RNN）研究相结合的工作，并建立了关键尺寸循环神经网络模型。利用该循环神经网络模型，开发了一种工艺条件筛选器以筛选合适的工艺条件。实验结果表明，关键尺寸模型的预测准确率超过93%，在筛选出的工艺条件下的光刻胶图案能够满足预设关键尺寸的要求。这项工作为精确的电子束光刻工艺建模开辟了新的视角，并为电子束光刻实验提供了指导。
