FMRG: Cyber: Manufacturing USA: Manufacturing of Next-Generation Perovskite Semiconductors at Scale

在过去的一个世纪里，半导体制造在很大程度上依赖于顺序沉积和去除材料层来制造集成器件。然而，逐层顺序处理对可用于器件顶层的工艺参数施加了限制，该工艺参数必须保持与底层的化学和热兼容性。这限制了在许多应用中设计精确接口的能力，特别是在将新兴功能材料与新的工艺约束相结合时。为了克服这些限制，未来制造研究基金(FMRG)探索了一种新的卤化物钙钛矿半导体层压方法，使功能和新设备架构的工程成为可能。这项技术的潜在应用是太阳能电池、LED和其他光电子器件。通过开发分布式制造的网络基础设施，生成经过训练的机器学习模型，以优化每个最终用户唯一的特定目标函数，从而支持中小型制造商(SMM)设计原型和扩大流程的能力。这项研究与教育和劳动力发展活动紧密结合，与当地劳动力发展组织和行业咨询委员会(IAB)建立了伙伴关系，以确定下一代网络制造工人的教育和培训需求，同时确保制造业劳动力多样化。该项目支持半导体制造和可再生能源的国家优先事项。FMRG研究的研究目标是使用一种新的层压方法来理解、建模和控制卤化物钙钛矿(HP)半导体制造过程中的工艺-结构-性能关系，该方法能够实现新的器件架构和材料组合，这些结构和材料组合目前无法使用传统的顺序沉积工艺。在这种方法中，器件半叠层可以在放松工艺约束的情况下独立地并行处理，并随后使用具有受控对准的连续叠层平台进行集成。通过将在线计量与物理信息数据驱动模型相结合，该项目对指导HP层压工艺的热-化学-机械机制有了基本的了解，从而实现了闭环工艺控制。开发了将高通量、低保真的在线测量数据流与低通量、高保真的异地表征方法连接起来的算法，这些方法在工业制造环境中是禁止的。通过开发降阶模型和使用联合学习方法优化过程参数，实现了物理信息的过程控制。网络制造平台能够自动生成不同(和非理想化)制造环境下的过程-结构-性能关系数据库，从而通过共享的网络基础设施实现预测性建模和过程优化。这个未来制造奖由土木工程、机械和制造创新(CMMI)和工程教育和中心(EEC)分部以及国家纳米技术创新倡议(NNI)特别研究计划支持。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。

For the past century, semiconductor manufacturing has relied heavily on sequential deposition and removal of material layers to fabricate integrated devices. However, sequential layer-by-layer processing imposes restrictions on the processing parameters that can be used for the top layers in the device, which must maintain chemical and thermal compatibility with the underlying layers. This limits the ability to engineer precise interfaces in many applications, especially when integrating emerging functional materials with new processing constraints. To overcome these limitations, this Future Manufacturing Research Grant (FMRG) explores a novel lamination approach for halide perovskite semiconductors that enables the engineering of functionality and new device architectures. Potential applications of this technology are solar cells, LEDs, and other optoelectronic devices. By developing the cyberinfrastructure for distributed manufacturing, trained machine learning models are generated to optimize a specified objective function that is unique to each end-user, thereby supporting the ability of small-to-medium manufacturers (SMMs) to prototype their designs and scale-up their processes. This research is closely integrated with education and workforce development activities, where partnerships with local workforce development organizations and an industry advisory board (IAB) are formed to identify the education and training needs of the next generation of cyber manufacturing workers, while ensuring a diverse manufacturing workforce. This project supports the national priorities of semiconductor manufacturing and renewable energy.The research objective of this FMRG research is to understand, model, and control the process-structure-property relationships during halide perovskite (HP) semiconductor manufacturing using a novel lamination approach that enables new device architectures and material combinations that are currently inaccessible using traditional sequential deposition processing. In this approach, device half-stacks can be independently processed in parallel with relaxed process constraints, and subsequently integrated using a continuous lamination platform with controlled alignment. By integrating in-line metrology with physics-informed data-driven models, the project develops a fundamental understanding of the thermo-chemo-mechanical mechanisms that guide the HP lamination process, which enables closed-loop process control. Algorithms are developed that bridge high-throughput, low-fidelity in-line metrology data streams with low-throughput, high-fidelity ex situ characterization methods, which are prohibitive in an industrial manufacturing setting. Physics-informed process control is enabled through development of reduced-order models and process parameter optimization using federated learning approaches. The cyber manufacturing platform enables automated generation of a database of process-structure-property relationships under diverse (and non-idealized) manufacturing environments, which enables predictive modeling and process optimization through a shared cyberinfrastructure.This Future Manufacturing award was supported by the Divisions of Civil, Mechanical and Manufacturing Innovation (CMMI) and Engineering Education and Centers (EEC) and by the National Nanotechnology Initiative (NNI) Special Studies Program.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.