Data-driven fault diagnosis with missing syndromes imputation for functional test through conditional specification

In the electronic system manufacturing process, the board-level functional test is recognized as the most significant step to prevent defective products from entering the market. In recent years, machine learning and data mining have proven to be efficient techniques in determining root cause from the problematic functional test result, especially when the integrated circuits (IC) are becoming increasingly highly-integrated. However, the test results are sometimes unavailable due to either abnormal ending of the test sequence or occasional system failures, which results in a decreased performance of data-driven diagnosis systems. In this paper, we propose a data imputation algorithm to predict the missing entries in the functional test result, by considering the correlation between test items with conditional specification. We evaluate our data imputation algorithm over the test results collected from three different stages of functional test on a line card used in the telecommunication system. The result shows that our proposed data imputation algorithm consistently outperforms other imputation techniques with various data-driven approaches in terms of diagnosing the root cause, increasing the diagnosis accuracy by an average of 28.13% compared to none data imputation, and 9.74% compared to the naive pass imputation.