*Limitations and Challenges*

Even though employing data mining techniques has been very beneficial for this industry, as shown by all the studies used in this review, several disadvantages of data mining still exist and are as follows:


This evolution of semiconductor manufacturing relies heavily on the big data explosion in order to cope with the abovementioned data limitations and challenges of the semiconductor industry. Especially, supporting greater volumes and lengthier archives of data has allowed many solutions to correctly portray system dynamics, significantly simplify intricate multivariate interactions of parameters, eliminate disturbances, and clean and overcome data quality challenges. Data mining algorithms in such types of solutions must be rewritten in order to benefit from the parallel computation allowed by the high

processing capacity and storage power with the purpose of processing data without consuming too much time. However, an enormous amount of data and a wide range of data mining techniques does not mean necessarily more predictive capability and insights [186]. Researchers and practitioners have to adapt data mining techniques in a manner so that these will be customized to specific applications in terms of data quality available data and objective, among others.

Overall, through this review, some light was shed over the possible applications of data mining techniques in semiconductor manufacturing. Yet, given the sheer number of steps that this production process has, and due to its complexity, the number of studies already made is still scarce. Big data and data mining allowed for original and innovative insights through the analysis of large amounts of data and presenting correlations and opportunities that were not previously noticed. However, decision makers must decide and which data should be collected and employed and which questions must be answered [149]. This signifies that the potential to apply these techniques in other subprocesses is enormous and is still left largely unexplored. Finally, by suffering constant and quick evolution, the need to adapt these techniques to the newer processes in semiconductor manufacturing is another opportunity to explore.
