实现亚2nm跨晶圆均匀性能的方法

Yang H. Ban, Leandro Medina, Michael Da Silva, Sebastian Naranjo, Meghali J. Chopra
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摘要

缩短工艺开发时间和加快产品上市时间是微电子工业长期面临的挑战。允许整个晶圆优化的蚀刻模型的开发将使制造商能够优化工艺设计流程并在单个晶圆运行之前预测工艺缺陷。晶圆均匀性优化面临的挑战包括晶圆上的各种特征、等离子体腔内多个尺度上的蚀刻变化、特征计量以及计算成本高昂的模型开发。使这些挑战更加复杂的是数据质量和时间/成本效益之间的权衡,不同工具提供的各种测量信息,以及人类收集的数据的稀疏性和不一致性。我们通过特征和晶圆级建模方法来解决这些挑战。首先,对各种蚀刻条件(例如,压力、气体成分、流速、温度、功率和偏置)进行实验。其次,基于OCD和/或截面SEM测量,在晶圆上的多个位置校准特征级模型。最后,利用校准后的模型来预测一组最佳工艺条件,以保持晶圆片的均匀性并满足配方目标。我们在FinFET应用中使用SandBox Studio™AI演示了该方法。具体来说,我们展示了在各种工艺条件下使用3D特征蚀刻的实验测量来快速和自动校准特征级模型。X-SEM数据的自动图像分割也在这里使用Weave®进行,以演示如何在开发环境中快速获取此类数据。然后,我们证明了降阶模型在预测最佳配方条件以提高整体配方性能方面的有效性。我们展示了如何使用这种混合计量计算方法,可以捕获产生89.2%晶圆的过程窗口。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A method for achieving sub-2nm across-wafer uniformity performance
Reducing process development time and speeding up time to market are perennial challenges in the microelectronics industry. The development of etch models that permit optimizations across the wafer would enable manufacturers to optimize process design flows and predict process defects before a single wafer is run. The challenges of across-wafer uniformity optimizations include the large variety of features across the wafer, etch variations that occur at multiple scales within the plasma chamber, feature metrology, and computationally expensive model development. Compounding these challenges are trade-offs between data quality and time/cost-effectiveness, the wide variety of measurement information provided by different tools, and the sparsity and inconsistency of human-collected data. We address these challenges with a feature and wafer level modeling approach. First, experiments are conducted for a variety of etch conditions (e.g., pressure, gas composition, flow rate, temperature, power, and bias). Second, a feature level model is calibrated at multiple sites across the wafer based on OCD and/or cross-sectional SEM measurements. Finally, the calibrated model is used to predict an optimal set of process conditions to preserve uniformity across the wafer and to meet recipe targets. We demonstrate the methodology using SandBox Studio™ AI for a FinFET application. Specifically, we show the rapid and automated calibration of feature level models using experimental measurements of the 3D feature etch at a variety of process conditions. Automated image segmentation of X-SEM data is also performed here for single case using Weave® to demonstrate how such data can be acquired quickly in a development environment. We then demonstrate the effectiveness of the reduced-order model to predict optimal recipe conditions to improve overall recipe performance. We show how, with this hybrid-metrology computational approach, a process window that yields 89.2% of the wafer can be captured.
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