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.

We present a novel approach called MINiature Interactive Offset Networks (or MINIONs). We use wafer map classification as an application example. A Minion is trained with a specially-designed one-shot learning scheme. A collection of Minions can be used to patch a master model. Experiment results are provided to explain the potential areas Minions can help and their unique benefits.

In this work, we consider learning a wafer plot recognizer where only one training sample is available. We introduce an approach called Manifestation Learning to enable the learning. The underlying technology utilizes the Variational AutoEncoder (VAE) approach to construct a so-called Manifestation Space. The training sample is projected into this space and the recognition is achieved through a pre-trained model in the space. Using wafer probe test data from an automotive product line, this paper explains the learning approach, its feasibility and limitation.