| Summary: | 博士 === 國立臺灣大學 === 電子工程學研究所 === 102 === Systematic defects and small delay defects (SDD) have become key challenges of yield and reliability due to shrinking geometry and increasing frequency in advanced technology. For yield and reliability improvement, physical failure analysis (PFA) is the most widely-used method to understand defect mechanisms. However, PFA is usually performed on a small part of failing dies because it is very time-consuming and expensive. Hence, a diagnosis technique which can correctly identify systematic defects and SDD to guide the selection of PFA dies is very much needed.
For systematic defect diagnosis, identification of culprit physical features that are responsible for yield loss is important for both yield enhancement and design-for-manufacturability (DFM) rule evaluation. Culprit physical features are certain defect-prone layout characteristic associated with various systematic defect mechanisms, such as stress, imperfect planarization, and other complex design-process interactions. To identify culprit physical features, we propose a systematic defect diagnosis technique, which considers physical feature in diagnosis of failing dies and then performs statistical analysis. To diagnose defects with complex failing behavior and multiple defects in the same die, we propose two physical-aware diagnosis techniques: physical feature-based diagnosis (PF-D) and multiple defect physical-aware diagnosis (MD-PhD). To cope with noise from random defects, a statistical technique
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