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Abstract 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.
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Mining Nonlinear Dynamics in Operational Data for Process Improvement
The emergence of nonlinear and nonstationary dynamics is common when multiple entities collaborate, compete, or interfere in manufacturing and service operations. Operational management calls on effective monitoring, modeling, and control of in-process nonlinear dynamics. This, in turn, can result in significant economic and societal benefits. Nevertheless, traditional reductionist approaches often fall short in comprehending nonlinear dynamical systems. Also, the theory of nonlinear dynamics is mainly studied in mathematics and physics. A critical gap remains in the knowledge base that pertains to integrating nonlinear dynamics research with operations engineering. The need to leverage nonlinear dynamics has become increasingly urgent for the development of high-quality products and services. This tutorial presents a review of nonlinear dynamics methods and tools for real-time system informatics, monitoring and control. Specifically, we discuss the characterization and modeling of recurrence dynamics, network dynamics, and self-organizing dynamics hidden in operational data for process improvement. Furthermore, we contextualize the theory of nonlinear dynamics with real-world case studies and discuss future opportunities to improve the monitoring and control of manufacturing and service operations. We posit this work will help catalyze more in-depth investigations and multidisciplinary research efforts at the intersection of nonlinear dynamics and data mining for operational excellence.
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- Award ID(s):
- 1617148
- PAR ID:
- 10552302
- Publisher / Repository:
- INFORMS
- Date Published:
- Journal Name:
- INFORMS TutORials in Operations Research
- ISSN:
- 978-0-9882856-1-8
- Page Range / eLocation ID:
- 109–132
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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