EAGER: Interpretable and Generalizable AI for Smart Manufacturing

这项探索性研究（EAGER）早期概念资助旨在概念化和研究一个通用的机器学习框架和相关的软件工具，这些工具需要对从全面运营的商业微电子制造设施获得的制造数据进行分类，并使用机器学习方法从这些数据中获得可靠的控制操作。由于无法获得所需的大量行业验证数据，与制造业相关的机器学习方法的研究一直受到挫折。该项目将探索新的机器学习方法的潜力，以揭示包含在数据中的隐性知识，以提高产量和生产力。该项目解决了机器学习（ML）在制造系统中应用的三个最关键的障碍：(1)缺乏对研究和开发适合于制造衍生数据的机器学习架构所需的大量数据的访问权限；(2)缺乏针对制造特定的ML方法来聚合和分类这些数据，以产生针对特定过程、机器或操作的训练ML系统量身定制的数据集；缺乏针对该数据设计并可以进行推断的ML架构；(3)制造工程师不愿信任“黑匣子”方法。该项目是与希捷科技的合作，以解决这三个障碍。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This EArly-concept Grant for Exploratory Research (EAGER) award is to conceptualize and research a generalized machine learning framework and the associated software tools needed to categorize manufacturing data acquired from a full-scale, operating commercial microelectronics fabrication facility and derive reliable control actions from that data using machine learning methods. Research on manufacturing-relevant machine learning methods has been frustrated by a lack of access to the large amount of industry-validated data needed to enable it. The project will explore the potential of new machine learning methods to reveal the implicit knowledge incorporated in that data to improve yield and productivity.The project addresses the three most critical impediments to the application of machine learning (ML) in manufacturing systems: (1) a lack of access to the massive amounts of data needed to research and develop machine learning architectures that are suited to manufacturing-derived data, (2) a lack of manufacturing-specific ML methods for aggregating and classifying that data to produce datasets tailored to training ML systems for specific processes, machines or operations and a lack of ML architectures that have been designed for and can make inferences using that data, and (3) a reluctance of manufacturing engineers to trust “black box” methods. The project is a collaboration with Seagate Technology to address all three impediments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.