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Schrunner Stefan, Bluder Olivia, Zernig Anja, Kaestner Andre, Kern Roman

A Comparison of Supervised Approaches for Process Pattern Recognition in Analog Semiconductor Wafer Test Data

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018

The semiconductor industry is currently leveragingto exploit machine learning techniques to improve and automate the manufacturing process. An essential step is the wafer test, where each single device is measured electrically, resulting in an image of the wafer. Our work is based on the hypothesis that deviations of production processes can be detected via spatial patterns on these wafermaps. Supervised learning methods are one possibility to recognize such patterns in an automated way - however, the training sample size is very low. In our work, we present and compare several methods for multiclass classification, which can deal with this limitation: multiclass decision trees, as well as decomposition methods like round robin and error- correcting output coding (ECOC). As elementary classifiers, we compare binary decision trees and logistic regression using an elastic net regularization. The evaluation shows that the decomposition methods outperform the multiclass decision tree regarding both, accuracy and practical demands.

Schrunner Stefan, Bluder Olivia, Zernig Anja, Kaestner Andre, Kern Roman

Markov Random Fields for Pattern Extraction in Analog Wafer Test Data

International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), IEEE, Montreal, Canada, 2017

In semiconductor industry it is of paramount im- portance to check whether a manufactured device fulfills all quality specifications and is therefore suitable for being sold to the customer. The occurrence of specific spatial patterns within the so-called wafer test data, i.e. analog electric measurements, might point out on production issues. However the shape of these critical patterns is unknown. In this paper different kinds of process patterns are extracted from wafer test data by an image processing approach using Markov Random Field models for image restoration. The goal is to develop an automated procedure to identify visible patterns in wafer test data to improve pattern matching. This step is a necessary precondition for a subsequent root-cause analysis of these patterns. The developed pattern ex- traction algorithm yields a more accurate discrimination between distinct patterns, resulting in an improved pattern comparison than in the original dataset. In a next step pattern classification will be applied to improve the production process control.
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