High-throughput and cost-efficient fabrication of intricate nanopatterns using top-down approaches remains a significant challenge. To overcome this limitation, advancements are required across various domains: patterning techniques, real-time and post-process metrology, data analysis, and, crucially, process control. We review recent progress in continuous, top-down nanomanufacturing, with a particular focus on data-driven process control strategies. We explore existing Machine Learning (ML)-based approaches for implementing key aspects of continuous process control, encompassing high-speed metrology balancing speed and resolution, modeling relationships between process parameters and yield, multimodal data fusion for comprehensive process monitoring, and control law development for real-time process adjustments. To assess the applicability of established control strategies in continuous settings, we compare roll-to-roll (R2R) manufacturing, a paradigmatic continuous multistage process, with the well-established batch-based semiconductor manufacturing. Finally, we outline promising future research directions for achieving high-quality, cost-effective, top-down nanomanufacturing and particularly R2R nanomanufacturing at scale.

中文翻译：

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连续纳米制造的最新进展：专注于机器学习驱动的过程控制


使用自上而下的方法高通量和成本效益地制造复杂的纳米图案仍然是一项重大挑战。为了克服这一限制，需要在各个领域取得进步：图形化技术、实时和后处理计量、数据分析，以及至关重要的过程控制。我们回顾了连续、自上而下的纳米制造的最新进展，特别关注数据驱动的过程控制策略。我们探索了现有的基于机器学习 （ML） 的方法，用于实施连续过程控制的关键方面，包括平衡速度和分辨率的高速计量、过程参数和产量之间的关系建模、用于全面过程监控的多模态数据融合以及用于实时过程调整的控制律开发。为了评估已建立的控制策略在连续设置中的适用性，我们将卷对卷 （R2R） 制造（一种典型的连续多阶段工艺）与成熟的基于批次的半导体制造进行了比较。最后，我们概述了实现高质量、成本效益、自上而下的纳米制造，特别是大规模 R2R 纳米制造的有前途的未来研究方向。