Good die prediction modelling from limited test items
Good die prediction modelling from limited test items
复制标题
从有限的测试项目中建立良好的模具预测模型
DOI：
10.1109/itc-asia.2018.00030
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Yoshiyuki Nakamura
中科院分区：
文献类型：
--
作者：
98.Takeru Nishimi;Yasuo Sato;Seiji kajihara;Yoshiyuki Nakamura
本文提出了一种基于机器学习技术的测试成本降低方法。该方法试图在试验过程中对已制造的模具中的好模具进行预测。如果芯片在完成所有测试过程之前被预测为良好，则该芯片将被允许发货，而无需经过剩余的测试过程，该过程包括昂贵的老化测试和最终测试。通过基于支持向量机的程序和K-折交叉验证，从选定测试项目的已知结果中建立了一个确定好的模具判断的预测模型。为了从业务效果的角度对该方法进行评估，我们还提出了新的评估指标--成本降低率和不良芯片逃逸率，使之能够确认面向零缺陷的测试成本的降低。对要求零缺陷的工业模具的测试数据的实验结果表明，该方法具有显著的可预测性，具有较高的测试成本降低能力。
This paper proposes a test cost reduction method using machine learning techniques. The proposed method tries to predict good dies among the manufactured dies on the way of test process. If a die is predicted as good before completing all of the test process, the die will be allowed to be shipped without going through the remaining test process which contains costly burn-in test and final test. By a SVM-based procedure together with K-fold cross validation, a prediction model to judge certainly good dies is created from known results of the selected test items. In order to evaluate the method in terms of the business effectiveness, we also propose new evaluation measures, "cost reduction rate" and "bad die escape rate", which enable to confirm zero-defect oriented test cost reduction. Experimental results obtained through test data for industrial dies requiring zero-defect show that the proposed method has significant predictability with high test cost reduction capability.