Harnessing big data analytics to improve semiconductor yield, reliability, and predictive maintenance

As the pace of technological advancement increases, the demand for novel solutions across fields is also burgeoning. Industries that rely on intricate and sophisticated systems of manual assembly lines are tasked with eradicating downtime, increasing output, and reducing production costs—all while ensuring the highest product quality. In tandem, upcoming technologies are driving change and improvement in industry. In this landscape, existing production processes are developed and enhanced through the use of cloud systems, data analytics, and connectivity. The goal is to gain insights and implement preventive measures. However, the increased reliance on data results in new challenges and possibilities that were not present before, thus creating a need for organizations to rethink strategy.

The semiconductor manufacturing sector is at the forefront of complexity and requires machinery that produces unique products of intricate design, for which only techniques not previously presented in the manufacturing world are used. Thus, existing approaches from industries that rely on machinery with tight and repeating cycles may not be feasible and might produce results that fail to achieve the expected outcome. Still, the market demands for more solutions from production lines that are increasingly complex themselves. A semiconductor fabrication plant is a building, commonly several stories high and covering more than 500.000 m2 in its entirety, comprising several hundreds of machines, dozens of modules, and expected to produce several thousands of wafers per day. Alternatively, a semiconductor back-end factory is smaller but with equal complexity (Parmar, 2021;  Kalusivalingam et al., 2022; Anang & Chukwunweike, 2024).

As the broadest area of industry, the semiconductor industry is also highly autonomous, whereby each machine is tasked with carrying out a subsection of the overall process, e.g., a lithographic process that deposits a layer of photoresist and enables designing at the nanoscale level. The back-end assembly used to produce and prepare the chips in the package is equally technological and demanding. Here, more than seventy individual machines handle photonic chips, usually more than 300 times per chip. Still, these machines are not perfect, and complexity emerges in the form of machinery breakdowns, production invalidation, error messaging, or human decision-making. In the highly automated world of serviced machinery, especially in semiconductor manufacturing, Big Data Analytics enables plant-wide data utilization turned into actionable insights (Siddiqui et al., 2024; Wright et al., 2024; Rath et al., 2025).