This paper introduces WM-Graph, a novel approach designed for flexible analytics of wafermaps. The key concept behind WM-Graph is the construction of a wafermap graph, where individual wafermaps are connected if they exhibit semi-equivalence. This graph-based structure allows a wide range of analytics to be performed using established graph algorithms. Unlike traditional multi-class classification methods, WM-Graph enables more versatile analyses, making it possible to answer complex, practical questions that would otherwise be difficult to address. We explain the technical innovations that underpin the WM-Graph approach and demonstrate how to perform certain analytical tasks with simple graph operations. The effectiveness of the WM-Graph approach is validated through experiments using the public WM-811K dataset and a proprietary dataset from a recent production line.

This paper presents key ideas behind IEA-Plot, a software framework designed to conduct test data analytics through chat. We use wafer-based data analytics as an application example to discuss the ideas. IEA-plot interacts with a user through a dialog and produces plots according to user instructions. At the core of IEA-Plot is a knowledge graph connecting a frontend natural language parser to a backend API. This knowledge graph captures our analytics knowledge in the specific context. Usage examples are presented based on test data collected from a recent production line.

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.

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.