We present a novel approach where wafer map pattern analytics are driven by natural language queries. At the core is a semantic parser that translates a user query into a meaning representation comprising instructions to generate a summary plot. The allowable plot types are pre-defined which serve as an interface that communicates user intents to the analytics software backend. Application results on wafer maps from a recent production line are presented to explain the capabilities and benefits of the proposed approach.
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Language Driven Analytics for Failure Pattern Feedforward and Feedback
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.
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- Award ID(s):
- 2006739
- NSF-PAR ID:
- 10465154
- Date Published:
- Journal Name:
- 2022 IEEE International Test Conference (ITC)
- Page Range / eLocation ID:
- 288 to 297
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ITC50671.2022.00037
