Collaborative Research: Modeling Reliability for Scale-Driven Degradation and Spatial Defects

合作研究：规模驱动的退化和空间缺陷的可靠性建模

• 批准号: 0654172
• 负责人: Yue Kuo
• 金额: $ 15.74万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0654172&HistoricalAwards=false
• 关键词: Collaborative; Research; Modeling; Reliability; Scale

This research aims to develop new methods for collecting and analyzing data related to defects and degradation of nano-sized devices. Specific problems in nano-manufacturing are addressed, such as reliability analysis of field emission displays and metal-oxide semiconductor (MOS) with sub 2 nm equivalent oxide thickness dielectrics. Statistical methods for modeling degradation and defects will include spatial point process modeling with change-points and random effects. Failure based models will consider nano-scale effects such as electron traps, interface states and the spatial relationship of defects across the oxide layer. The interdisciplinary research team includes expertise in chemical engineering, industrial engineering and statistics, and MOS devices will be fabricated as well as characterized for reliability issues. Experimental results will be used and verified in the development of theoretical models.Results will create a more flexible framework for depicting the spatial distribution of defects (e.g., pair-potential Markov point process), which will be crucial for accurate reliability modeling. With this approach, state-of-the-art statistical models for nano-reliability will include hierarchical modeling, order-restricted inference, change-point regression, bias-adjusted bootstrap sampling and random effects modeling. The proposed study is physics-based from the PI's nano-electronics laboratories. Although nano-science has rapidly developed in chemistry and material science, reliability engineering and data analysis techniques for nano devices have not been sufficiently developed, especially in the United States. If successful, the outcome of this grant should help to change that, and the greatest impact could be the adaptation of advanced statistical analysis for nano-scale research. This research should also catalyze further statistical modeling in nanofabrication beyond the field of reliability.

这项研究旨在开发新的方法来收集和分析与纳米尺寸器件的缺陷和退化相关的数据。纳米制造中的具体问题，如场发射显示器和金属氧化物半导体（MOS）的可靠性分析与亚2纳米等效氧化物厚度的电阻。建模退化和缺陷的统计方法将包括变点和随机效应的空间点过程建模。基于故障的模型将考虑纳米尺度的影响，如电子陷阱，界面状态和整个氧化层的缺陷的空间关系。跨学科研究团队包括化学工程，工业工程和统计学方面的专业知识，MOS器件将被制造并表征可靠性问题。实验结果将用于理论模型的开发和验证。结果将创建一个更灵活的框架来描述缺陷的空间分布（例如，对潜在的马尔可夫点过程），这将是至关重要的准确的可靠性建模。通过这种方法，最先进的纳米可靠性统计模型将包括分层建模，顺序限制推理，变点回归，偏差调整自助抽样和随机效应建模。拟议的研究是基于PI的纳米电子实验室的物理学。虽然纳米科学在化学和材料科学方面发展迅速，但纳米器件的可靠性工程和数据分析技术尚未得到充分发展，特别是在美国。如果成功，这笔赠款的结果应该有助于改变这一现状，而最大的影响可能是将先进的统计分析应用于纳米尺度研究。这项研究还应该在可靠性领域之外催化纳米纤维的进一步统计建模。