标题： 参数化算子推理在半导体制造中用于模拟净化过程 显示英文标题

标题： Parametric Operator Inference to Simulate the Purging Process in Semiconductor Manufacturing

摘要： 本研究展示了参数化算子推理（OpInf）——一种非侵入式降阶建模（ROM）技术，该技术学习高保真模型的低维表示——在半导体制造中清洗过程数值模型中的应用。利用OpInf框架的数据驱动特性，我们旨在使用计算流体力学（CFD）模拟数据预测等离子体增强化学气相沉积（PECVD）腔室内的流场。我们的模型通过排除等离子体动力学和化学反应简化了系统，但仍捕捉到清洗流动行为的关键特征。参数化OpInf框架基于入口处不同氩气质量流量和不同出口压力学习了九个ROM。然后，它对这些ROM进行插值以预测系统在25种参数组合下的行为，其中包括训练中未见过的16种场景。参数化OpInf ROM在训练数据的36%上训练，在64%上测试，显示了整个参数域上的准确性，最大误差为9.32%。此外，与全阶模型CFD模拟相比，ROM在线计算速度提高了约142倍。这些OpInf ROM可用于快速准确地预测PECVD腔室中的清洗流动，从而有助于半导体制造中的有效粒子污染控制。 显示英文摘要

摘要： This work presents the application of parametric Operator Inference (OpInf) -- a nonintrusive reduced-order modeling (ROM) technique that learns a low-dimensional representation of a high-fidelity model -- to the numerical model of the purging process in semiconductor manufacturing. Leveraging the data-driven nature of the OpInf framework, we aim to forecast the flow field within a plasma-enhanced chemical vapor deposition (PECVD) chamber using computational fluid dynamics (CFD) simulation data. Our model simplifies the system by excluding plasma dynamics and chemical reactions, while still capturing the key features of the purging flow behavior. The parametric OpInf framework learns nine ROMs based on varying argon mass flow rates at the inlet and different outlet pressures. It then interpolates these ROMs to predict the system's behavior for 25 parameter combinations, including 16 scenarios that are not seen in training. The parametric OpInf ROMs, trained on 36\% of the data and tested on 64\%, demonstrate accuracy across the entire parameter domain, with a maximum error of 9.32\%. Furthermore, the ROM achieves an approximate 142-fold speedup in online computations compared to the full-order model CFD simulation. These OpInf ROMs may be used for fast and accurate predictions of the purging flow in the PECVD chamber, which could facilitate effective particle contamination control in semiconductor manufacturing.