Language Driven Analytics for Failure Pattern Feedforward and Feedback
Language Driven Analytics for Failure Pattern Feedforward and Feedback
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DOI：
10.1109/itc50671.2022.00037
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Min Jian Yang;Y. Zeng;Li-C. Wang
中科院分区：
文献类型：
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作者：
Min Jian Yang;Y. Zeng;Li-C. Wang
在分析晶圆地图的背景下，我们提出了一种新的方法，使分析由用户查询驱动。分析上下文包括两个方面：（1）基于它们的故障模式对晶片图进行分组，以及（2）对于在晶片探测器处发现的故障模式，检查以查看是否存在与来自最终测试的结果（前馈）和来自E测试的结果（反馈）的相关性。我们介绍了语言驱动的分析，并展示了后端的正式语言模型如何在前端实现自然语言查询。该方法被应用于分析测试数据，从最近的产品线，有趣的发现强调解释的方法和它的使用。
In the context of analyzing wafer maps, we present a novel approach to enable analytics to be driven by user queries. The analytic context includes two aspects: (1) grouping wafer maps based on their failure patterns and (2) for a failure pattern found at wafer probe, checking to see whether there is a correlation to the result from the final test (feedforward) and to the result from the E-test (feedback). We introduce language driven analytics and show how a formal language model in the backend can enable natural language queries in the frontend. The approach is applied to analyze test data from a recent product line, with interesting findings highlighted to explain the approach and its use.