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

这个促进工程变革和公平进步的研究思路（BRITE）枢轴奖支持研究开发一种新的方法来制造柔性光电子-太阳能电池，发光二极管和传感器用于健康和健康监测。这项创新技术使用闪光灯提供短脉冲能量来烧结超细颗粒，引发化学反应并形成新材料。用光代替传统的热源，如烤箱，可以大大减少时间和能源，加快制造产量并降低成本。但是，寻找优化材料性能的加工条件主要是通过试错来完成的，这是一种昂贵而低效的方法。相比之下，尖端的机器学习方法指导该项目寻找制造柔性设备薄膜的最佳加工条件。今天，环太平洋国家主导着柔性光电制造业;这个项目使美国更具竞争力。该研究涉及许多技术学科，包括材料加工和表征、设备制造和测试、数据分析和机器学习以及先进制造。该项目为本科生提供实践研究经验。此外，我们还培养了一支多元化的科学、技术、工程和数学人才队伍，包括各级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工艺，这需要多次迭代来优化。该项目研究了使用闪光灯的光代替加热炉进行薄膜加工，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烧结颗粒，引发化学反应，并转换材料。来自光脉冲的能量优先被薄膜吸收，导致选择性加热，而下面的衬底保持低于其工作温度。因此，这种方法能够在不能承受高温的塑料基板上进行处理，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与起始材料特性密切相关的工艺参数，因此实现所需的质量结果是一个具有挑战性的优化问题。传统的每次改变一个变量的方法在探索整个参数空间时效率低下，因此耗时且昂贵。研究团队的方法是根据输入空间的明智采样收集初始实验结果，并应用先进的数据分析技术，在探索未经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，并从机器学习结果中开发了用于薄膜加工优化的基于物理的模型。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。

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.