Harnessing process variations for optimizing wafer-level probe-test flow

Retraction:Reliability assurance in early-life-failure test through improved nearest neighbor regression

“…This shift undermines the effectiveness of a simple stuck-at based test solution. Data-based classification algorithms have improved continuously with the change of defects, minimize yield loss [11,12,13,14,15,16,17], ML can be used to distinguish between marginal defects and process variation defects based on circuit delay, depend on different delay distribution. The classification results can be able to locate the defects and identify the root cause.…”

“…In [14], hidden characteristics of test data that make a correlation between dies and test items were investigated, but did not consider data overfitting. In [16], a wafer test flow was optimized with a graphical model and in [17], defect characteristics by wafer mapping were D e l e t e d investigated, but did not consider the marginal defects. A method in [18] predicts test results using Support Vector Machine (SVM) with a weighted dynamic time warping kernel function, but did not consider classification [19].…”

“…As we can see, most of previous works are based on conventional fault models [11,12,13,14,15,16,17,18], lack of research on marginal defects and process variation defects with machine learning classification methods. To bridge this gap and formally show that, in a quest to reduce ELF and increase reliability, the work presented in this paper combines the concepts of regression and KNN algorithm with data preprocessing method.…”