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

FMRG：网络：美国制造：大规模制造下一代钙钛矿半导体

• 批准号: 2328010
• 负责人: Neil Dasgupta
• 金额: $ 300万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328010&HistoricalAwards=false
• 关键词: FMRG; Cyber; Manufacturing; USA; Next; FMRG; Cyber; Manufacturing; USA; Next

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.

在过去的一个世纪中，半导体制造业在很大程度上依赖于依次的沉积和将材料层的去除到织物集成设备上。但是，顺序逐层处理不可能限制可以用于设备中顶层的处理参数，这些处理参数必须维持与基础层的化学和热兼容性。这限制了在许多应用程序中设计精确接口的能力，尤其是在将新兴的功能材料与新处理约束集成在一起时。为了克服这些限制，这项未来的制造研究补助金（FMRG）探索了卤化物钙钛矿半导体的一种新型层压方法，该方法能够实现功能和新设备体系结构的工程。该技术的潜在应用是太阳能电池，LED和其他光电设备。通过开发用于分布式制造的网络基础结构，生成了训练有素的机器学习模型，以优化每个最终用户独特的指定目标函数，从而支持小型到中等制造商（SMMS）原型制作其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融合，在该活动中，与当地劳动力发展组织和行业顾问委员会（IAB）建立了伙伴关系，以确定下一代网络制造的教育和培训需求。工人，同时确保制造工人不同。该项目支持半导体制造和可再生能源的国家优先事项。这项FMRG研究的研究目标是使用新型的层压方法来理解，模型和控制卤化物钙钛矿（HP）半导体制造过程中的过程结构构造关系，该方法使用新型的层压方法，该方法可实现新的设备体系结构和材料的处理，这些方法是使用新的材料组合，可使用传统的序列化。在这种方法中，设备半堆栈可以与放松的过程约束并行地独立处理，并随后使用具有控制对准的连续层压平台进行集成。通过将内线计量学与物理知识的数据驱动模型整合在一起，该项目对指导HP层压过程的热化学机械机制有了基本的了解，从而实现了闭环过程控制。开发了算法，该算法是桥梁高通量，低保真在线计量数据流具有低通量，高保真的EXATU表征方法，这些方法在工业制造环境中被禁止。通过开发减少订单模型和使用联合学习方法的过程优化，可以实现物理知识的过程控制。网络制造平台可以在多样化（和非理想化）制造环境下自动生成过程过程中的过程，该数据库可以通过共享的Cyber​​inFrasture奖（NNI）特殊研究计划。该奖项反映了NSF的法定任务，并且我们是否使用基金会的知识分子优点和更广泛的影响评论标准来评估我们是否被认为是诚实的支持。