
Abstract The identification of defects plays a key role in the semiconductor industry as it can reduce production risks, minimize the effects of unexpected downtimes and optimize the production process. A literature review protocol is implemented and latest advances are reported in defect detection considering wafer maps towards quality control. In particular, the most recent works are outlined to demonstrate the use of AI-technologies in wafer maps defect detection. The popularity of these technologies is then presented in the form of visualizing graphs. This enables the identification of the most popular and most prominent ML-methods that can be exploited for the purposes of Industry 4.0.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 15 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
