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

在过去的世纪，半导体制造严重依赖于材料层的顺序沉积和去除来制造集成器件。然而，顺序逐层处理对可用于器件中的顶层的处理参数施加了限制，其必须保持与下层的化学和热相容性。这限制了在许多应用中设计精确界面的能力，特别是在将新兴功能材料与新的加工限制相结合时。为了克服这些限制，未来制造研究基金（FMRG）探索了一种用于卤化物钙钛矿半导体的新型层压方法，该方法能够实现功能和新器件架构的工程设计。该技术的潜在应用是太阳能电池、LED和其他光电器件。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指定目标函数，从而支持中小型制造商（SMM）对其设计进行原型设计并扩大其流程的能力。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）建立合作伙伴关系，以确定下一代网络制造工人的教育和培训需求，同时确保多样化的制造业劳动力。该项目支持半导体制造和可再生能源的国家优先事项。该FMRG研究的研究目标是了解，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，使用新的层压方法，实现新的器件架构和材料组合，目前无法使用传统的顺序沉积工艺。在这种方法中，器件半堆叠可以在放松的工艺约束的情况下并行地独立处理，并且随后使用具有受控对准的连续层压平台来集成。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。开发了将高吞吐量、低保真度的在线计量数据流与低吞吐量、高保真度的非原位表征方法桥接的算法，所述非原位表征方法在工业制造环境中是禁止的。通过使用联邦学习方法开发降阶模型和过程参数优化，实现物理信息过程控制。网络化制造平台能够自动生成各种条件下的工艺-结构-性能关系数据库，（和非理想化的）制造环境，通过共享的网络基础设施实现预测建模和流程优化。该未来制造奖得到了民用，机械和制造创新（CMMI）和工程教育和中心（EEC），并由国家纳米技术倡议（NNI）特别研究计划。该奖项反映了NSF的法定使命，并被认为是值得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的支持。

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.