Workshops on Smart Manufacturing with Open and Scaled Data Sharing in Semiconductor and Microelectronics Manufacturing; Virtual and In-Person; Washington, DC; October/November 2023

半导体和微电子制造业开放和规模化数据共享的智能制造研讨会将召集来自微电子制造和人工智能（AI）和机器学习（ML）研究和从业者社区的专家，包括来自相关公司，大学，联邦机构，国家实验室和美国制造研究所的代表。该研讨会的动机是解决先进制造中对制造数据的关键需求，这是在国家科学技术理事会先进制造小组委员会和机器学习和人工智能小组委员会的主持下举行的前一系列研讨会的关键发现。无法访问上下文化、分类和可重用的制造数据被认为是智能制造和人工智能对美国经济的全部潜力的关键行业障碍。制造业没有数据使用或交付基础设施来提高许多其他行业的生产力。这些讲习班的目的是制定一项战略，以获取跨公司的微电子生产数据，同时保持专有数据的安全。这样的策略有望通过为机器学习研究人员提供访问特征良好的数据来提高美国微电子制造公司的生产力。需要这些数据来创新新的机器学习架构，特别适合对制造过程数据进行分类。该研讨会的独特之处在于其专注于详细的行业数据，对这些数据的分析定义了关键的业务和技术决策。该研讨会在强调更广泛的数据科学和计算机科学观点方面也是独一无二的，其结构和格式有助于建立跨社区的观点和兴趣。研讨会将从个人社区讨论开始，以分享立场和理解词汇。这些会议将发展成为一系列跨社区的虚拟圆桌会议，让制造业和AL/ML研究人员与微电子行业的利益相关者一起定义特定的业务和技术问题。这些成果将在随后在华盛顿地区举行的面对面会议上一并讨论。面对面会议将：（1）定义并商定促进协作数据共享和模型构建的机制，并利用当前技术和/或定义新方法的需求，（2）构建行业，学术和政府合作，以及（3）创建微电子制造行业跨公司数据聚合的行动计划草案。虚拟会议和现场会议将包括专业撰稿人，以协助组织委员会记录每届会议的结果并使其合理化，从而指导其余会议的议程。现场会议之后将是一个虚拟的一般审查和评论会议，涉及所有参与者。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The Workshops on Smart Manufacturing with Open and Scaled Data Sharing in Semiconductor and Microelectronics Manufacturing will convene experts from the microelectronics manufacturing and artificial intelligence (AI) and machine learning (ML) research and practitioner communities, including representatives from relevant companies, universities, federal agencies, national laboratories, and the Manufacturing USA Institutes. The workshop is motivated by and addresses the critical need for manufacturing data in advanced manufacturing, a key finding of the previous workshop series, held under the auspices of the Subcommittee on Advanced Manufacturing and the Subcommittee on Machine Learning and Artificial Intelligence of the National Science and Technology Council. The inability to access manufacturing data that are contextualized, categorized, and discoverable was identified as a critical industry-wide impediment to smart manufacturing and the full potential of AI to the US economy. Manufacturing does not have the data use or delivery infrastructure for enhancing productivity that has evolved in many other industries. The aim of the workshops is to formulate a strategy for accessing cross-company microelectronics production data while keeping proprietary data secure. Such a strategy promises to improve the productivity of US microelectronics manufacturing companies by providing machine learning researchers with access to the well-characterized data. These data are needed to innovate new machine learning architectures specifically suited to categorizing manufacturing process data. The workshop is unique in its focus on detailed industry data the analysis of which defines critical business and technology decisions. The workshop is also unique in emphasizing the broader data science and computer science perspectives with a structure and format that facilitates building cross-community views and interests. The workshop will start with individual community discussions to share positions and understand vocabularies. These will grow into a series of several cross community virtual roundtables to engage manufacturing and AL/ML researchers with stakeholders from the microelectronics industry in defining specific business and technical questions. These outcomes will be addressed together in a subsequent in-person session in the Washington, DC area. The in-person session will: (1) define and agree on mechanisms for facilitating collaborative data sharing and model building at scale with current technologies and/or by defining needs for new methods, (2) frame an industry, academic, and government collaboration, and (3) create a draft action plan for cross-company data aggregation in the microelectronics manufacturing industry. The virtual and in-person sessions will include professional writers to assist the organizing committee in taking notes and rationalizing the findings from each session to guide the agenda for the remaining sessions. The in-person session will be followed by a virtual general review and comment session that involves all participants.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.