Swarm intelligence in automated electrical wafer sort classification

The semiconductor manufacturing domain is by no doubt a rich and challenging environment for the application of machine learning. Some of the demanding characteristic of semiconductor data include high dimensionality, mixtures of categorical and numerical data, non-randomly Gaussian data, non-Gaussian and multi-modal distributions, highly non-linear complex relationships, noise and outliers in both x and y dimensions, temporal dependencies, etc. These challenges are becoming particularly crucial as the quantity of available data is growing dramatically. This paper addresses the problem of automatic wafer manufacturing process error detection based on electrical wafer sort (EWS) parametric tests. A wafer-to-wafer analysis is presented that automatically detect possible errors in the manufacturing process that causes systematic damage to the product as it passes through some step in the process. Possible causes are equipment mishandling, operator error, material issues such as contamination, etc. These manufacturing errors are reflected on the physical and electrical properties of the wafers, which are measured by the parametric tests in the EWS process. The core of this research is a novel classifier system based on the benefits arising from the interaction between evolutionary algorithms and artificial neural networks. Experimental results demonstrates that this system is able to detect defective wafers with an accuracy of 82%. Prior to the EWS classification, the proposed system is evaluated with three classification benchmark problems: Iris dataset, Australian credit card problem, and the Pumas diabetes dataset. The obtained results are compared with classifier´s outcomes available in the literature.