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首页> 外文会议>Conference on Metrology, Inspection, and Process Control for Semiconductor Manufacturing >Methods to Overcome Limited Labeled Data Sets in Machine Learning-Based Optical Critical Dimension Metrology
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Methods to Overcome Limited Labeled Data Sets in Machine Learning-Based Optical Critical Dimension Metrology

机译：克服基于机器学习的光学关键维度计量中的有限标记数据集的方法

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摘要

With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical Critical Dimension (OCD) time to solution to a critical point. Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical modeling. However, expensive and limited reference data or labeled data set necessary for ML to learn from often leads to under- or overlearning, limiting its wide adoption. In this paper, we explore techniques that utilize process information to supplement reference data and synergizing physical modeling with ML to prevent under- or overlearning. These techniques have been demonstrated to help overcome the constraint of limited reference data with use cases in challenging OCD metrology for advanced semiconductor nodes.
机译：利用半导体器件的激进缩放，耦合更紧密计量误差预算的器件结构的复杂性越来越多地驱动了光学关键尺寸（OCD）时间来解决方案到临界点。 由于其极快的转变，机器学习（ML）已成功应用于OCD计量作为传统物理建模的替代解决方案。 然而，ML的昂贵且有限的参考数据或标记的数据集，以便从经常导致或覆盖的情况下学习，限制其广泛的采用。 在本文中，我们探讨了利用过程信息来补充参考数据的技术，并用ML协同物理建模以防止欠压或覆盖。 已经证明了这些技术有助于克服有限参考数据的限制与使用病例在提高高级半导体节点的挑战性OCD计量中。

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