**5. Discussion**

After analyzing all the studies collected in the sample, a few trends begin to be noticed. First, that studies regarding data mining applications in subprocesses such as ICs and mask design are very scarce. The same occurs with studies addressing wafer cutting, cleaning drying, and polishing, while edge rounding and lapping subprocess has no dedicated study. This is better illustrated by Figure 10 in which a representation of several studies depicting data mining applications in several subprocesses of semiconductor manufacturing can be seen. It is noticeable that the majority of studies are concentrated in 5–6 major steps. A few studies do not specify in which subprocess data mining techniques are applied, and these are not represented in Figure 10.

**Figure 10.** Representation of several studies depicting data mining applications in several subprocesses of semiconductor manufacturing.

Another trend visible in the analyzed literature is the diverse use of data mining techniques. The application of data mining in semiconductor manufacturing has a different focus depending on the subject areas concerning the manufacturing processes. However, most articles address mainly the issues of quality control, maintenance, and production. Predictive techniques, using algorithms as regression or decision trees, are often used in semiconductor literature to estimate wafer quality [81], fault detection [121,136], or cycletime [170]. Classification techniques in quality control arise as a way to classify defects [83], failures in bin maps [91], or production lots [131]. The exploration of yield loss causes [84]

or failure diagnostics [98] is performed using techniques as rule induction, decision trees, and association rules.

Many opportunities and improvements can still be made. For example, the semiconductor companies could employ the internet of things and sensors to empower industrial units with the capability of interpreting data and transmitting analytics, in real time, to an application that could provide insights and alerts to whom it may concern [174]. This will allow these players to gather a high amount of data. However, even though internet of things and data mining applications represent a key opportunity for semiconductor manufacturing companies—one that they should start to pursue as soon as possible, while the use of data mining in the sector is still developing under the current upgrading environment. Nevertheless, the effectiveness and scale of the internet of things implementation, and with it a comprehensive use of data mining techniques, could depend on how fast industry players can overcome some challenges [175]. In order to persevere and being able to accompany the change speed and challenges, semiconductor companies are required to adapt rapidly. Taking into account this dynamic, industrial units should embrace digitalization in an agile manner as well [176].
