ABSTRACT

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.