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Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this work, we go one step further and analyse test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps.To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: (A) classical image processing and restoration techniques in combination with sophisticated feature engineering and (B) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modelled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results.

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Classification has been tackled by a large number of algorithms, predominantly following a supervised learning setting. Surprisingly little research has been devoted to the problem setting where a dataset is only partially labeled, including even instances of entirely unlabeled classes. Algorithmic solutions that are suited for such problems are especially important in practical scenarios, where the labelling of data is prohibitively expensive, or the understanding of the data is lacking, including cases, where only a subset of the classes is known. We present a generative method to address the problem of semi-supervised classification with unknown classes, whereby we follow a Bayesian perspective. In detail, we apply a twostep procedure based on Bayesian classifiers and exploit information from both a small set of labeled data in combination with a larger set of unlabeled training data, allowing that the labeled dataset does not contain samples from all present classes. This represents a common practical application setup, where the labeled training set is not exhaustive. We show in a series of experiments that our approach outperforms state-of-the-art methods tackling similar semi-supervised learning problems. Since our approach yields a generative model, which aids the understanding of the data, it is particularly suited for practical applications.

Reliable semiconductor devices are of paramount importance as they are used in safety relevant applications. To guarantee the functionality of the devices, various electrical measurements are analyzed and devices outside pre-defined specification limits are scrapped. Despite numerous verification tests, risk devices (Mavericks) remain undetected. To counteract this, remedial actions are given by statistical screening methods, such as Part Average Testing and Good Die in Bad Neighborhood. For new semiconductor technologies it is expected that, due to the continuous miniaturization of devices, the performance of the currently applied screening methods to detect Mavericks will lack accuracy. To meet this challenge, new screening approaches are required. Therefore, we propose to use a data transformation which analyzes information sources instead of raw data. First results confirm that Independent Component Analysis extracts meaningful measurement information in a compact representation to enhance the detection of Mavericks.

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