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

本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有关的数据。讨论了纳米制造中的具体问题，如场发射显示器的可靠性分析和具有亚2nm等效氧化物厚度电介质的金属氧化物半导体（MOS）。退化和缺陷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级效应，如电子陷阱、界面状态和氧化物层缺陷的空间关系。跨学科的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器件的制造和可靠性问题将得到表征。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，对势马尔可夫点过程），这对于准确的可靠性建模至关重要。采用这种方法，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、调整偏差的自举抽样和随机效应建模。这项提议的研究是基于物理学的，来自PI的纳米电子实验室。虽然纳米科学在化学和材料科学领域发展迅速，但纳米器件的可靠性工程和数据分析技术还没有得到充分的发展，特别是在美国。如果成功，这项资助的结果应该有助于改变这种状况，最大的影响可能是将先进的统计分析应用于纳米尺度的研究。这项研究也应该催化在可靠性领域以外的纳米制造中进一步的统计建模。

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.