隨著半導體技術的進步，時序邊界日益縮小，小延遲缺陷已成為系統晶片測試中的主要課題。此外，由於其對電路時序的影響細微且受製程變異影響，使得小延遲缺陷的診斷更具挑戰性。本文提出一種小延遲缺陷診斷方法，透過多個延遲值的注入提升故障模擬解析度，並藉由不一致加權評分方式增強候選缺陷的排序準確性。該方法考慮了時序不確定性與製程變異的影響，能夠實現更準確的缺陷定位。
實驗結果顯示，本方法在診斷準確性方面有顯著提升。此外，我們引入一項新的評估指標預期命中次數 (EHC），進一步驗證本方法在實務中實施物理故障分析（PFA）時的強效性。與傳統診斷指標不同，EHC可直接量化成功識別實際缺陷位置的機率，能夠同時反映候選排序品質與同分情況對診斷結果的影響。因此，EHC提供了一種更貼近實務分析需求的診斷成效評估方式。

As semiconductor technology advances, small delay defects (SDDs) have become a major concern in System-on-Chip (SoC) testing due to shrinking timing margins. Moreover, diagnosing SDDs is challenging due to their subtle impact on circuit timing and the existence of process variations. This thesis presents an SDD diagnosis method that improves fault simulation resolution through multiple delay injections and enhances candidate ranking using a mismatch-weighted (MW) score. The proposed approach accounts for timing uncertainty and process variation effects, enabling more robust and accurate defect localization.
Experimental results demonstrate significant improvements in diagnostic accuracy. In addition, evaluation using a new metric called the Expected Hit Count (EHC) confirms the method’s strong effectiveness under practical physical failure analysis (PFA) constraints. Unlike conventional diagnostic metrics, EHC directly quantifies the probability of successfully identifying the actual defect location, reflecting both candidate ranking quality and the impact of ranking ties. This allows a more realistic assessment of diagnostic success Experimental results demonstrate significant improvements in diagnostic accuracy. In addition, evaluation using a new metric called the Expected Hit Count (EHC) confirms the method’s strong effectiveness under practical physical failure analysis (PFA) constraints. Unlike conventional diagnostic metrics, EHC directly quantifies the probability of successfully identifying the actual defect location, reflecting both candidate ranking quality and the impact of ranking ties. This allows a more realistic assessment of diagnostic success in real-world failure analysis scenarios.

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