EAGER: Transforming Additive Nanomanufacturing with Machine Learning

光电基板形成各种功能器件中的关键部件，其中基板用于允许光通过，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柔性版本，例如射频识别（RFID）标签、人造皮肤和电子纸。高性能光电器件的可用性极大地影响了可穿戴设备、物联网等技术，从而为国家经济和安全做出了贡献。目前，刚性玻璃基板通常与夹层一起使用。然而，这些涂层不提供跨越宽范围的波长或角度的反射，并且缺乏其他期望的多功能性。这个早期概念的探索性研究（EAGER）计划奖赠款支持研究，以创建一个框架，将机器学习方法应用于纳米制造过程。一个具体的目标是利用机器学习和优化方法在表面上设计和构建纳米光子结构，以实现不同的光学特性，如防雾和抗菌。增材纳米制造是一种多功能的方法，用于创建具有纳米尺度特征的复杂3D结构。由于许多表面工程设计和功能是可能的，因此需要机器学习方法。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模和可扩展的蚀刻和沉积工艺。该研究活动是高度多学科的，并体现了工业工程和材料工程在纳米制造研究的未来和培训未来的劳动力中发挥的独特作用。该项目创建了一个框架，将纳米制造方法，如反应离子蚀刻，与机器学习和优化工具，物理模拟，和多功能表征，以展示具有高性能光子管理特性（例如光致变色和雾度管理）的耐用且柔性的纳米结构光电基板。大多数最近的工作在表面工程的多功能纳米结构涂层的各种各样的刚性和新兴的柔性光电器件涉及传统的试错法，提供，充其量，分散和有限的系统研究的小区域的参数空间没有任何嵌入的历史知识。在证明制造工艺的可扩展性方面存在重大挑战。这项研究旨在测试一个假设，即与当前的增量方法相比，机器学习和优化框架可以用于更快速地设计和工程化光电基板。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分析。通过创建新的机器学习方法克服了当前的局限性，这些方法简洁地学习纳米结构-添加剂NanoManufacturing-属性关系，并具有跨领域概括的能力。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。

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.