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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711577
• 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. Built on top of our recent advances in statistical neuro-space mapping and cognition driven technologies for RF/microwave design, this research explores new frontiers in EM/multiphysics based statistical modeling and yield optimization. This research will develop new optimization algorithms exploiting fast parametric EM model with or without coarse engineering models, dramatically cutting the computational expenses of yield optimization of EM structures. The research will create unified parametric modeling algorithms for EM structures combining space mapping and knowledge-based neural network models. New dynamic statistical neuro-space mapping algorithm will be developed for statistical modeling of nonlinear devices covering the statistical behavior of both high- and low-frequency (such as trapping effects) responses. The research also aims to open a new frontier in modeling and design by extending EM-based statistical design to multiphysics-based statistical design. A new class of space mapping optimization algorithms with mapping between EM space and multiphysics space for microwave optimization will be introduced. The long term direction is a unified EM/multiphysics based methodology for fast and accurate statistical modeling and yield optimization for next generation high-frequency electronic design. The long-term impact will be faster design cycle, lower design cost, better design quality and increased manufacturing yield. It contributes to creating new knowledge and training of highly qualified technical personnel in areas of high-frequency electronic design.

这项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的功能、复杂性、信号速度和带宽的增加，构建模块组件和子系统的设计规范变得更加严格。这反过来又使得不可避免的制造公差和组件和子系统的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。直接考虑参数不确定性的统计建模和良率优化成为设计过程的重要组成部分。然而，传统的统计建模和良率优化技术已经足够成熟，可以用于基于等效电路的设计，但由于计算成本过高，这些技术不适用于当今基于电磁(EM)/多物理场的设计。为了实现EM和基于多物理场的产量驱动设计，需要一种新的统计建模和优化范例。拟议的研究将解决这些挑战。