Advanced Statistical Modeling and Optimization Technologies for Yield-Driven Design of High-Frequency Electronic Circuits

用于高频电子电路产量驱动设计的先进统计建模和优化技术

• 批准号: RGPIN-2017-06420
• 负责人: Zhang, Qijun
• 金额: $ 2.7万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637291
• 关键词: Advanced; Statistical; Modeling; Optimization; Technologies

The objective of this research is to develop next generation technologies for statistical modeling and optimization of high-frequency electronic components and packages in wireless and wireline communication systems. With increasing functionality, complexity, signal speed and bandwidth in wireless and wireline communication systems, the design specifications for the building block components and subsystems become more stringent. This in turn makes the effects from unavoidable manufacturing tolerances and process uncertainties in components and subsystems more pronounced in the overall system performance, affecting production yield and posing challenges in design. Statistical modeling and yield optimization directly taking into account the uncertainties in parameters as part of the design process become important. However, conventional statistical modeling and yield optimization techniques that are mature enough for equivalent circuit based design are not effective for today’s electromagnetic (EM)/multiphysics-based design because of the prohibitive computational cost. A new statistical modeling and optimization paradigm is necessary to enable EM and multiphysics based yield-driven design. The proposed research will address these challenges.

本研究的目标是开发下一代技术，用于无线和有线通信系统中高频电子元件和封装的统计建模和优化。随着无线和有线通信系统中的功能性、复杂性、信号速度和带宽的增加，构建块组件和子系统的设计规范变得更加严格。这反过来又使得组件和子系统中不可避免的制造公差和工艺不确定性对整个系统性能的影响更加明显，影响生产良率并对设计提出挑战。作为设计过程的一部分，直接考虑参数不确定性的统计建模和产量优化变得重要。然而，传统的统计建模和成品率优化技术，是足够成熟的等效电路为基础的设计是不是有效的，今天的电磁（EM）/多物理为基础的设计，因为高昂的计算成本。一个新的统计建模和优化范式是必要的，使EM和多物理场的成品率驱动的设计。拟议的研究将解决这些挑战。