Enhancing Scalability of Virtual Metrology: A Deep Learning-Based Approach for Domain Adaptation

Natalie Gentner, A. Kyek, Yao Yang, Mattia Carletti, Gian Antonio Susto
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Abstract

One of the main challenges in developing Machine Learning-based solutions for Semiconductor Manufacturing is the high number of machines in the production and their differences, even when considering chambers of the same machine; this poses a challenge in the scalability of Machine Learning-based solutions in this context, since the development of chamber-specific models for all equipment in the fab is unsustainable. In this work, we present a domain adaptation approach for Virtual Metrology (VM), one of the most successful Machine Learning-based technology in this context. The approach provides a common VM model for two identical-in-design chambers whose data follow different distributions. The approach is based on Domain-Adversarial Neural Networks and it has the merit of exploiting raw trace data, avoiding the loss of information that typically affects VM modules based on features. The effectiveness of the approach is demonstrated on real-world Etching.
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增强虚拟计量的可扩展性:一种基于深度学习的领域自适应方法
为半导体制造开发基于机器学习的解决方案的主要挑战之一是生产中的机器数量众多,并且它们之间存在差异,即使考虑到同一台机器的腔室;在这种情况下,这对基于机器学习的解决方案的可扩展性提出了挑战,因为针对晶圆厂中所有设备的特定腔室模型的开发是不可持续的。在这项工作中,我们提出了虚拟计量(VM)的领域自适应方法,这是在此背景下最成功的基于机器学习的技术之一。该方法为数据遵循不同分布的两个相同的设计室提供了一个通用的VM模型。该方法基于域对抗神经网络,它具有利用原始跟踪数据的优点,避免了通常影响基于特征的VM模块的信息丢失。该方法的有效性在实际刻蚀中得到了验证。
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