BRITE Pivot: Machine Learning Accelerated Optimization of Flash Lamp Processed Thin-films for Flexible Optoelectronic Applications

BRITE Pivot：机器学习加速优化用于柔性光电应用的闪光灯加工薄膜

• 批准号: 2135203
• 负责人: Julia Hsu
• 金额: $ 48.28万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135203&HistoricalAwards=false
• 关键词: BRITE; Pivot; Machine; Learning; Accelerated

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot award supports research to develop a new approach to the manufacturing of flexible optoelectronics -- solar cells, light-emitting diodes, and sensors for health and wellness monitoring. The innovative technique uses a flash lamp to deliver short pulses of energy to sinter ultrafine particles, initiate chemical reactions, and form new materials. Replacing traditional heat sources such as ovens with light can greatly reduce time and energy, accelerating manufacturing output and decreasing costs. But finding the processing conditions that optimize materials properties has mostly been done by trial and error, an expensive, inefficient method. By contrast, cutting-edge machine learning approaches guide this project's search for the best processing conditions in fabricating thin films for flexible devices. Today, Pacific Rim nations dominate flexible optoelectronic manufacturing; this project makes the U.S. more competitive. The research involves many technical disciplines, including materials processing and characterization, device fabrication and testing, data analytics and machine learning, and advanced manufacturing. The project provides undergraduates with hands-on research experience. It also develops a diverse Science, Technology, Engineering, and Mathematics workforce by including at all levels women and other under-represented groups.Making high-quality materials often requires a high-temperature annealing process, which takes many iterations to optimize. This project investigates the use of light from a flash lamp instead of furnace heating for thin film processing and adopts machine learning approaches to accelerate process optimization. Photonic curing uses millisecond pulses of intense broadband light to sinter particles, initiate chemical reactions, and transform materials. The energy from the light pulses is preferentially absorbed by the thin film, leading to selective heating, while the underlying substrate remains below its working temperature. Hence, this approach enables processing on plastic substrates which cannot withstand high temperatures, and, therefore, is particularly useful in flexible optoelectronics fabrication. Because photonic curing involves many processing parameters that are intimately coupled with starting material properties, achieving desired quality outcomes is a challenging optimization problem. Traditional varying-one-variable-at-a-time methods are inefficient in exploring the entire parameter space and are thus time-consuming and expensive. The research team’s approach is to collect initial experimental results based on the judicious sampling of input space and apply advanced data analytic techniques that balance between exploring untested phase space and fine-tuning conditions to achieve global optimization. The relationships between input parameters and output improvements for photonic curing are revealed, and physics-based models for thin film processing optimization are developed from the machine learning results.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.

推动工程转型和公平进步研究想法奖(BRITE)Pivot奖支持研究开发一种制造柔性光电子产品的新方法--太阳能电池、发光二极管和用于健康和健康监测的传感器。这项创新技术使用闪光灯来发射短脉冲能量，以烧结超细颗粒，引发化学反应，并形成新材料。用光取代传统的热源，如烤箱，可以大大减少时间和能源，加快制造产量，降低成本。但是，寻找优化材料性能的工艺条件主要是通过反复试验来完成的，这是一种昂贵、低效的方法。相比之下，尖端的机器学习方法指导该项目在为柔性设备制造薄膜时寻找最佳工艺条件。今天，环太平洋国家主导着灵活的光电制造；这个项目使美国更具竞争力。这项研究涉及许多技术学科，包括材料加工和表征、器件制造和测试、数据分析和机器学习以及先进制造。该项目为本科生提供了实践研究经验。它还培养了一支多样化的科学、技术、工程和数学劳动力队伍，包括所有级别的女性和其他代表性不足的群体。制造高质量的材料通常需要高温退火过程，这需要多次迭代才能优化。该项目研究了使用闪光灯的光来代替炉子加热进行薄膜加工，并采用机器学习方法来加速工艺优化。光子固化使用毫秒脉冲的强宽带光来烧结颗粒，引发化学反应，并改变材料。来自光脉冲的能量优先被薄膜吸收，导致选择性加热，而底层衬底保持在其工作温度以下。因此，这种方法能够在不耐高温的塑料衬底上进行加工，因此在柔性光电子制造中特别有用。由于光子固化涉及许多工艺参数，这些参数与起始材料的性能密切相关，因此实现期望的质量结果是一个具有挑战性的优化问题。传统的一次改变一个变量的方法在探索整个参数空间时效率低下，因此既耗时又昂贵。研究团队的方法是根据输入空间的明智采样收集初步实验结果，并应用先进的数据分析技术，这些技术在探索未测试的相空间和微调条件之间进行平衡，以实现全局优化。揭示了光子固化的输入参数和输出改进之间的关系，并根据机器学习的结果开发了基于物理的薄膜处理优化模型。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。