EAGER: Transforming Additive Nanomanufacturing with Machine Learning

光电子衬底是各种功能器件中的关键部件，在这些器件中，衬底的功能是允许光线通过，同时保护器件免受周围环境的影响。应用包括显示器、太阳能电池、智能手机、平板电脑、发光二极管(LED)，以及这些光电设备的新兴灵活版本，如射频识别(RFID)标签、人造皮肤和电子纸。高性能光电子器件的出现极大地影响了可穿戴设备、物联网等技术，从而为国家的经济和安全做出了贡献。目前，刚性玻璃基板通常使用减反射层。然而，这些涂层不能在广泛的波长或角度范围内提供减反射，并且缺乏其他所需的多功能。这一早期概念探索性研究(EARGER)项目奖支持创建将机器学习方法应用于纳米制造过程的框架的研究。一个具体的目标是利用机器学习和优化的方法来设计和构建表面上的纳米光子结构，以实现不同的光学性能，如防雾和抗菌。添加纳米制造是一种创建具有纳米级特征的复杂三维结构的通用方法。由于许多表面工程设计和功能都是可能的，因此需要一种机器学习方法。该项目研究纳米制造方法，涉及半导体器件制造行业中常用的无掩膜和可扩展的蚀刻和沉积工艺。这一研究活动是高度多学科的，体现了工业工程和材料工程在未来纳米制造研究和培训未来劳动力方面所发挥的独特作用。该项目创建了一个框架，将纳米制造方法(如反应离子刻蚀)与机器学习和优化工具、物理模拟和多功能表征相结合，以展示耐用和灵活的纳米结构光电子基板，具有高性能的光子管理特性，如减反射和雾霾管理。最近在用于各种刚性和新兴柔性光电子器件的多功能纳米结构涂层的表面工程方面的大多数工作都涉及到传统的试错方法，这种方法充其量是对参数空间的小区域进行零散和有限的系统研究，而没有任何嵌入的历史知识。在展示制造过程的可伸缩性方面存在重大挑战。这项研究试图验证这样一个假设，即与目前的增量式方法相比，机器学习和优化框架可以更快地设计和设计光电子基板。集成了机器学习方法来拟合实验数据，预测新结构的性能，并为额外的实验提供启发式方法。目前的局限性是通过创造新的机器学习方法来克服的，这些方法简洁地学习纳米结构-添加剂纳米制造-性能关系，并具有跨域泛化的能力。开发机器学习模型是为了确定如何使用添加剂纳米制造在玻璃和塑料上制造3D纳米结构表面。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.