Discovering Correlated Parameters in Semiconductor Manufacturing Processes: A Data Mining Approach

Abstract

Abstract-Data mining tools are nowadays becoming more and more popular in the semiconductor manufacturing industry, and especially in yield-oriented enhancement techniques. This is because conventional approaches fail to extract hidden relationships between numerous complex process control parameters. In order to highlight correlations between such parameters, we propose in this paper a complete knowledge discovery in databases (KDD) model. The mining heart of the model uses a new method derived from association rules programming, and is based on two concepts: decision correlation rules and contingency vectors.

A Data-Driven Approach for Identifying Possible Manufacturing Processes and Production Parameters That Cause Product Defects: A Thin-Film Filter Company Case Study

“…The third-category studies both identify poor-quality processes and machines, and extract the rules of manufacturing process parameters from manufacturing data [19], [21], [50]. The process engineers can then use their domain knowledge to extract, select, and implement the most useful rules to fulfill product yield enhancement.…”

“…The process engineers can then use their domain knowledge to extract, select, and implement the most useful rules to fulfill product yield enhancement. Casali and Ernst [50] integrated a decision tree with the chi-square test to evaluate the correlation of multiple and complex parameters for semiconductor process control. Chien et al [19] adopted a multidimensional principal component analysis (PCA) to extract key indicators and subsequently used the k-nearest neighbor algorithm for grouping purposes and diagnosing and classifying process errors.…”

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

“…On this basis, the related process parameters can be adjusted to ensure future quality based on post hoc diagnosis [110][111][112][113][114][115][116][117][118]121]. Some studies combined with sequential pattern mining to identify the sequence association events between different operations during the manufacturing [119,120]. Moreover, more than hundred test items and millions of rows of data for wafers will be generated after testing, per day.…”

“…Some high-level languages such as C/C++ [94,120] and Visual Basic [70][71][72][73][75][76][77] were used for SOM, fuzzy c-means, fuzzy logic, ANN, and the combination of these approaches for its flexibility for researcher to design or combine particular methodologies considering domain knowledge in handling and analyzing the data. Meanwhile, some platforms such as the online system [79], fab-wide FDC [80], VM system [83], online time series prediction system [88], and wafer bin of map clustering and classification systems [117] have been developed for different tasks of AEC/APC based on high-level languages.…”

A knowledge discovery in databases approach for industrial microgrid planning

“…KDD techniques application to manufacturing processes usually address the search of correlations between process variables [41][42][43][44] among production process data and control parameters. The discoveries of these correlations allow planners to incorporate the generated knowledge into a model of the manufacturing process, which can be exploited with multiple purposes.…”