BRITE Pivot: Accelerating Manufacturing and Realization of Perovskite Micro-Light Emitting Device (Micro-LED) Displays through Data-driven Learning

BRITE Pivot：通过数据驱动学习加速钙钛矿微发光器件 (Micro-LED) 显示器的制造和实现

• 批准号: 2227285
• 负责人: Lih Lin
• 金额: $ 60万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227285&HistoricalAwards=false
• 关键词: BRITE; Pivot; Accelerating; Manufacturing; Realization

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot grant supports research aiming to advance augmented- and virtual-reality (AR/VR) display technologies through exploring new materials and manufacturing driven by machine learning approaches. AR/VR technologies connect people with the digital world through immersive experiences, and present unbounded applications in many industries. However, these technologies face many obstacles which hinder their prevalence, with one notable impediment being optical clarity and resolution. To this end, the industry is actively investing in micro-light emitting device (micro-LED) technology, which preserves the high optical quality of emissive LEDs while reducing the pixel size by nearly an order of magnitude over the current state-of-the-art. Nevertheless, manufacturing of micro-LED displays through conventional semiconductor fabrication processes is extremely time consuming and labor intensive, which leads to prohibitive costs, yields, and production times. This award supports fundamental research on new micro-LED technologies utilizing solution-processed perovskite materials to overcome these challenges and optimizing the device performance through machine learning. The project facilitates the acquisition of knowledge in a new field, machine learning, to supplement established expertise in perovskite optoelectronics. The vast scope of applications of AR/VR ranges from education and healthcare to manufacturing and defense. Therefore, results from this research benefit several U.S. industrial sectors, thus enhancing national security and prosperity. The multi-disciplinary research entails hands-on education and training experiences closely related to industrial applications, while promoting diversity, equity, and inclusion in project participation.While perovskite devices have quickly made significant impacts in photovoltaics, their progress for light-emitting applications has been lagging due to degradation of the materials and devices under high electric fields. Enhancing perovskite LED performance requires simultaneous optimization of material synthesis, device structures and operating conditions. Machine learning (ML) offers the possibility of quickly converging to a globally optimized result through a multi-variate data-driven learning process. This project aims to develop a neural network-based ML approach to facilitate manufacturing of perovskite micro-LED displays with high optical quality and stability. Furthermore, the ML process is designed and executed with the aim of optimizing perovskite LED output luminance and stability, with proper input features. Through the researched project, features pertaining to perovskite LEDs are identified and collected from various databases, and, along with additional experimental data, are fed into the ML models, which drive the solution processing of the perovskites. Additionally, a micro-patterning technology is developed to address the challenge of patterning perovskites with high resolution through photolithography.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.

这一促进工程变革和公平进步的研究思路（BRITE）Pivot赠款支持旨在通过探索机器学习方法驱动的新材料和制造来推进增强和虚拟现实（AR/VR）显示技术的研究。AR/VR技术通过沉浸式体验将人们与数字世界联系起来，并在许多行业中呈现出无限的应用。然而，这些技术面临许多阻碍其普及的障碍，其中一个显著的障碍是光学清晰度和分辨率。为此，业界正在积极投资微型发光器件微发光二极管（micro-LED）技术，其保持了发射型LED的高光学质量，同时将像素尺寸比当前技术水平减小了近一个数量级。然而，通过常规半导体制造工艺制造微发光二极管显示器是极其耗时且劳动密集的，这导致了过高的成本、产量生产时间。该奖项支持利用溶液处理的钙钛矿材料进行新型micro-LED技术的基础研究，以克服这些挑战，并通过机器学习优化器件性能。该项目有助于获得机器学习这一新领域的知识，以补充钙钛矿光电子学方面的现有专业知识。AR/VR的应用范围广泛，从教育和医疗保健到制造业和国防。因此，这项研究的结果使美国的几个工业部门受益，从而增强了国家安全和繁荣。多学科研究需要与工业应用密切相关的实践教育和培训经验，同时促进项目参与的多样性，公平性和包容性。虽然钙钛矿器件在光化学领域迅速产生了重大影响，但由于高电场下材料和器件的退化，其在发光应用方面的进展一直滞后。增强钙钛矿LED性能需要同时优化材料合成、器件结构和操作条件。机器学习（ML）通过多变量数据驱动的学习过程提供了快速收敛到全局优化结果的可能性。该项目旨在开发一种基于神经网络的ML方法，以促进具有高光学质量和稳定性的钙钛矿微LED显示器的制造。此外，ML工艺的设计和执行旨在优化钙钛矿LED输出亮度和稳定性，并具有适当的输入特性。通过研究项目，从各种数据库中识别和收集与钙钛矿LED有关的特征，并将其与其他实验数据一起沿着输入ML模型，从而驱动钙钛矿的溶液处理。此外，还开发了一种微图案化技术，以应对通过光刻技术实现高分辨率钙钛矿图案化的挑战。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。