EAGER: Interpretable and Generalizable AI for Smart Manufacturing

EARLY概念探索性研究（EAGER）奖旨在概念化和研究广义机器学习框架和相关软件工具，这些工具用于对从全规模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该数据中获得可靠的控制动作。 由于无法获得大量经过行业验证的数据，制造相关机器学习方法的研究一直受到阻碍。该项目将探索新机器学习方法的潜力，以揭示数据中包含的隐含知识，从而提高产量和生产力。该项目解决了机器学习（ML）应用的三个最关键障碍在制造系统中：（1）缺乏对研究和开发适合制造业衍生数据的机器学习架构所需的大量数据的访问，（2）缺乏特定于制造业的ML方法来聚合和分类这些数据，以生成为特定过程训练ML系统而定制的数据集，机器或操作以及缺乏为这些数据设计并可以使用这些数据进行推断的ML架构，以及（3）制造工程师不愿意信任“黑箱”方法。 该项目是与希捷技术公司合作解决所有三个障碍的项目。该奖项反映了NSF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.