SHF: Small: Data Learning Framework for Diagnosis Based Yield Optimization

SHF：小型：基于诊断的产量优化的数据学习框架

• 批准号: 0915259
• 负责人: Li-Chung Wang
• 金额: $ 33万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915259&HistoricalAwards=false
• 关键词: SHF; Small; Data; Learning; Framework

ABSTRACTIn the semiconductor industry, manufacturing yield, measured as the percentage of salable products produced, is a key metric that determines the financial success of a product line. Low yield translates into increased design cost, delayed time-to-market, and reduced productivity. When low yield occurs, tremendous engineering resources are spent to diagnose and resolve the problems. This project proposes to develop a novel data learning framework that greatly improves the efficiency and effectiveness of the diagnosis and resolution process. The framework consists of a newly developed software infrastructure that interfaces with the existing Electronic Design Automation (EDA) and silicon test software infrastructures, through the design and silicon test data they produce. A collection of data learning software tools and methodologies that analyze said data are utilized to automatically extract knowledge for yield improvement. The research is integrated with educational activities to develop course and tutorial materials released to the industry for broad impact, a state-of-the-art laboratory for education, and a research program to attract undergraduate and underrepresented students. The research strives to achieve a comprehensive understanding of state-of-the-art design and manufacturing practices including anticipated issues in the future, and to accomplish multidisciplinary studies merging knowledge from EDA, silicon test, data mining, and machine learning. Knowledge discovered through this research will provide the industry with a clear direction on where to invest resources to better cope with yield related issues in future ultra nanometer manufacturing technologies. The framework is designed to efficiently improve yield, which helps improve productivity in the semiconductor design industry.

摘要在半导体行业中，生产良率（以生产的可销售产品的百分比来衡量）是决定产品线财务成功的关键指标。低良率意味着增加设计成本、延迟上市时间和降低生产率。当低产量发生时，大量的工程资源被花费在诊断和解决问题上。该项目提出开发一种新的数据学习框架，大大提高诊断和解决过程的效率和有效性。该框架包括一个新开发的软件基础设施，通过它们产生的设计和硅测试数据与现有的电子设计自动化（EDA）和硅测试软件基础设施接口。 利用分析所述数据的数据学习软件工具和方法的集合来自动提取用于产量改进的知识。该研究与教育活动相结合，以开发课程和教程材料，发布给行业，以产生广泛的影响，一个最先进的教育实验室，以及一个吸引本科生和代表性不足的学生的研究计划。该研究致力于全面了解最先进的设计和制造实践，包括未来预期的问题，并完成多学科研究，融合EDA，硅测试，数据挖掘和机器学习的知识。通过这项研究发现的知识将为行业提供一个明确的方向，即在哪里投入资源，以更好地科普未来超纳米制造技术中与产量相关的问题。该框架旨在有效地提高良率，这有助于提高半导体设计行业的生产率。