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Fast Detection of Manufacturing Systematic Design Pattern Failures Causing Device Yield Loss

机译:快速检测导致设备良率损失的制造系统设计模式故障

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Starting from the 45nm technology node, systematic defectivity has a significant impact on device yield loss with each new technology node. The effort required to achieve patterning maturity with zero yield detractor is also significantly increasing with technology nodes. Within the manufacturing environment, new in-line wafer inspection methods have been developed to identify device systematic defects, including the process window qualification (PWQ) methodology used to characterize process robustness. Although patterning is characterized with PWQ. methodology, some questions remain: How can we demonstrate that the measured process window is large enough to avoid design-based defects which will impact the device yield? Can we monitor the systematic yield loss on nominal wafers? From device test engineering point of view, systematic yield detractors are expected to be identified by Automated Test Pattern Generator (ATPG) test results diagnostics performed after electrical wafer sort (EWS). Test diagnostics can identify failed nets or cells causing systematic yield loss. Convergence from device failed nets and cells to failed manufacturing design pattern are usually based on assumptions that should be confirmed by an electrical failure analysis (EFA). However, many EFA investigations are required before the design pattern failures are found, and thus design pattern failure identification was costly in time and resources. With this situation, an opportunity to share knowledge exists between device test engineering and manufacturing environments to help with device yield improvement. This paper presents a new yield diagnostics flow dedicated to correlation of critical design patterns detected within manufacturing environment, with the observed device yield loss. The results obtained with this new flow on a 28nm technology device are described, with the defects of interest and the device yield impact for each design pattern. The EFA done to validate the design pattern to yield correlation are also presented, including physical cross sections. Finally, the application of this new flow for systematic design pattern yield monitoring, compared to classic inline wafer inspection methods, is discussed.
机译:从45nm技术节点开始,系统缺陷率对每个新技术节点的器件良率损失都有重大影响。随着技术节点的增加,用零成品率减幅器实现图案成熟度所需的工作也大大增加。在制造环境中,已经开发出新的在线晶圆检查方法来识别设备的系统缺陷,包括用于表征工艺鲁棒性的工艺窗口鉴定(PWQ)方法。虽然图案化是用PWQ来表征的。在方法论上,仍然存在一些问题:我们如何证明测得的工艺窗口足够大,可以避免会影响器件良率的基于设计的缺陷?我们可以监控名义晶圆的系统良率损失吗?从设备测试工程的角度来看,有望通过电子晶圆分选(EWS)后执行的自动测试图案生成器(ATPG)测试结果诊断来识别系统的良率降低因素。测试诊断程序可以识别导致系统良率损失的故障网或电池。从设备故障的网络和单元到故障的制造设计模式的收敛通常基于应该由电气故障分析(EFA)确认的假设。但是,在发现设计模式故障之前,需要进行许多EFA调查,因此,识别设计模式故障在时间和资源上都是昂贵的。在这种情况下,存在在设备测试工程和制造环境之间共享知识的机会,以帮助提高设备良率。本文提出了一种新的良率诊断流程,该流程专门用于在制造环境中检测到的关键设计模式与观察到的器件良率损失之间的相关性。描述了在28nm技术的设备上使用这种新流程获得的结果,以及每种设计模式的关注缺陷和设备良率的影响。还介绍了为验证设计模式与产量相关性所做的EFA,包括物理截面。最后,与传统的在线晶圆检查方法相比,讨论了这种新流程在系统设计图案成品率监控中的应用。

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