In this paper, we propose a new learning-guided approach for diagnosis of intra-cell defects that may occur in customer returns. In the first part of the paper, only static defects modeled by stuck-at faults have been assumed. Several supervised learning algorithms were considered, with various levels of efficiency. In the second part of the paper, we have extended the previous work by dealing with more sophisticated (i.e., dynamic) defects. This time, we concentrated on a Bayesian classification method used for predicting the nature (likelihood to be a good candidate) of each new data instance (defect) that has to be evaluated during the diagnosis process. Results obtained on benchmark circuits, and comparison with a commercial cell-aware diagnosis tool, demonstrate the efficiency of the proposed approach in terms of accuracy and resolution.

中文翻译：

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基于监督学习的客户退货细胞感知缺陷诊断

在本文中，我们提出了一种新的学习引导方法，用于诊断客户退货中可能出现的细胞内缺陷。在本文的第一部分，仅假设了由固定故障建模的静态缺陷。考虑了几种具有不同效率水平的监督学习算法。在论文的第二部分，我们通过处理更复杂的（即动态的）缺陷扩展了之前的工作。这一次，我们专注于贝叶斯分类方法，用于预测在诊断过程中必须评估的每个新数据实例（缺陷）的性质（成为良好候选者的可能性）。在基准电路上获得的结果，以及与商业细胞感知诊断工具的比较，证明了所提出的方法在准确性和分辨率方面的效率。