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Physically-aware analysis of systematic defects in integrated circuits

机译:对集成电路中系统缺陷的物理感知分析

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Systematic defects due to design-process interactions are a significant component of integrated circuit (IC) yield loss in nano-scale technologies. Test structures do not adequately represent the product in terms of feature diversity and feature volume, and therefore are unable to identify all the systematic defects that will affect a product over its manufacturing lifetime. This paper describes a comprehensive methodology that addresses the prevention and identification of systematic defects. For prevention, a method called RADAR (Rule Assessment of Defect-Affected Regions) has been developed for measuring the effectiveness of design-for-manufacturability (DFM) rules in preventing systematic defects that is based on volume diagnosis data. A second method called LASIC (Layout Analysis for Systematic Identification using Clustering), also based on volume diagnosis data, has been developed for identifying systematic defects that escape DFM. To validate RADAR and LASIC, a fast and accurate defect simulation framework called SLIDER (Simulation of Layout-Injected Defects for Electrical Responses) has been developed. SLIDER generates virtual failure data with known defect characteristics. Experiments involving two industrial chips and virtual failure data from SLIDER demonstrate the effectiveness of RADAR and LASIC.
机译:由于设计过程相互作用而导致的系统缺陷是纳米级技术中集成电路(IC)良率损失的重要组成部分。测试结构在特征多样性和特征量方面不能充分代表产品,因此无法识别将在产品的整个使用寿命内影响产品的所有系统缺陷。本文介绍了解决系统缺陷的预防和识别方法的综合方法。为了预防,已经开发了一种称为RADAR(缺陷影响区域规则评估)的方法,该方法用于基于体积诊断数据来测量可制造性设计(DFM)规则在预防系统缺陷方面的有效性。还开发了第二种方法,也称为LASIC(使用聚类进行系统识别的布局分析),该方法也基于体积诊断数据,用于识别逃避DFM的系统缺陷。为了验证RADAR和LASIC,已经开发了一种快速准确的缺陷仿真框架,称为SLIDER(用于电响应的布图缺陷的仿真)。 SLIDER生成具有已知缺陷特征的虚拟故障数据。涉及两个工业芯片和来自SLIDER的虚拟故障数据的实验证明了RADAR和LASIC的有效性。

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