Improve Volume Physical-Aware Diagnosis via Active Pattern Sampling
Jiaxing Gao, Baohua Wang, Yin Zhang, Yu Huang, Xiaotian Ding, Weimin Zhang · 2023
Volume diagnosis is an essential step in diagnosis driven yield analysis. Generally, diagnosis run time increases when more failing patterns are collected in a fail log. In order to improve diagnosis throughput, pattern sampling, which only uses a subset of the failing patterns, is a common practice in volume diagnosis. Compared to the results achieved by using all the failing patterns, traditional pattern sampling has a negative effect on the diagnosis accuracy and resolution. In this paper, we propose a layout-aware active pattern sampling method which improves the quality of diagnosis in terms of accuracy and resolution over the traditional pattern sampling method. Meanwhile, it achieves higher diagnosis throughput compared with the non-sampling methodology. Diagnostic results on four industrial designs show that the average suspects per symptom are reduced by 10.77% ~35.86 % compared to the traditional pattern sampling method. Besides, our algorithm has an advantage of identifying more actual defective locations compared with the traditional sampling or non-sampling method.