EAGER: Transforming Additive Nanomanufacturing with Machine Learning

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

Optoelectronic substrates form a critical component in a variety of functional devices where the substrate functions to allow light to pass through and, at the same time, protect the device from the ambient environment. Applications include displays, solar cells, smart phones, tablets, light emitting diodes (LEDs), as well as emerging flexible versions of these optoelectronic devices such as radio frequency identification (RFID) tags, artificial skin, and e-paper. The availability of high performance, optoelectronic devices greatly impacts technologies such as wearables, the Internet of Things, and more, thus contributing to the nation's economy and security. Currently, rigid glass substrates are typically used with an antireflection layer. However, these coatings do not provide for antireflection across a wide range of wavelengths or angles and lack other desired multi-functionality. This EArly-concept Grants for Exploratory Research (EAGER) program award supports research to create a framework for applying machine learning methods to nanomanufacturing processes. A specific goal is to utilize the machine learning and optimization approach to design and construct nanophotonic structures on surfaces to achieve different optical properties such as anti-fogging and anti-bacterial. Additive nanomanufacturing is a versatile method to create complex 3D structures with nano-scale features. Since many surface engineering designs and functions are possible, a machine learning approach is needed. The project studies nanomanufacturing approaches involving maskless and scalable etching and deposition processes that are commonly used in the semiconductor device fabrication industry. This research activity is highly multidisciplinary and exemplifies the unique role that industrial engineering and materials engineering play in the future of nanomanufacturing research and in training the future workforce. The project creates a framework that integrates nanomanufacturing methods, such as reactive ion etching, with machine learning and optimization tools, physical simulations, and multi-functional characterizations to demonstrate durable and flexible nanostructured optoelectronic substrates with high performance photon management properties, such as antireflection and haze management. Most of the recent work in surface engineering of multi-functional nanostructure coatings for a wide variety of rigid and emerging flexible optoelectronic devices has involved traditional trial-and-error approaches that offer, at best, fragmented and limited systematic studies of small regions of the parameter space absent any embedded historical knowledge. Major challenges exist in demonstrating the scalability of manufacturing processes. This research seeks to test the hypothesis that a machine learning and optimization framework can be utilized to more rapidly design and engineer optoelectronic substrates compared to current incremental approaches. Machine learning methods are integrated to fit experimental data, predict the performance of new structures, and provide heuristics for additional experiments. Current limitations are overcome through the creation of new machine learning methods that succinctly learn nanostructure-additive NanoManufacturing-property relationships with the ability to generalize across domains. Machine learning models are developed to determine how to manufacture 3D nanostructured surfaces on glass and plastics using additive nanomanufacturing. 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.