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

这项研究旨在开发新的方法来收集和分析与纳米器件缺陷和退化相关的数据。讨论了纳米制造中的具体问题，如场发射显示器和具有小于2 nm等效氧化层厚度的金属氧化物半导体(MOS)介质的可靠性分析。对退化和缺陷进行建模的统计方法将包括具有变化点和随机效应的空间点过程建模。基于失效的模型将考虑纳米尺度的效应，如电子陷阱、界面态和氧化层上缺陷的空间关系。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，将制造MOS器件，并针对可靠性问题进行表征。实验结果将在理论模型的开发中使用和验证。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，对势马尔可夫点过程)，这将是准确的可靠性建模的关键。在这种方法下，纳米可靠性的最新统计模型将包括分层建模、阶数限制推理、变点回归、偏差调整的自举抽样和随机效应建模。这项拟议的研究是基于PI的纳米电子实验室的物理基础上的。尽管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还没有得到足够的发展，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点，最大的影响可能是将先进的统计分析应用于纳米级研究。这项研究还应该促进在可靠性领域以外的纳米制造领域的进一步统计建模。

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.