###### Abstract  
The manufacturing industry is facing increasing volatility, uncertainty, complexity, and ambiguity, while still requiring high delivery reliability to meet customer demands. This is especially challenging for complex job shops in the semiconductor industry, where the manufacturing process is highly intricate, making it difficult to predict the consequences of changes. Although simulation has proven to be an effective tool for optimizing manufacturing processes, reference data sets and models often produce disparate and incomparable results. CoJoSim is introduced in this article as a reference model for semiconductor manufacturing, along with an associated reference implementation that accelerates the implementation and application of the reference model. CoJoSim can serve as a testbed and gold standard for other implementations. Using CoJoSim, different dispatching rules are evaluated to demonstrate an improvement of almost 15 percentage points in adherence to delivery dates compared to the reference. Findings emphasize the importance of optimizing setup time, particularly in products with high variance, as it significantly impacts adherence to delivery dates and throughput. Moving forward, future applications of CoJoSim will evaluate additional dispatching rules and use cases. Combining CoJoSim with dispatching methods that integrate manufacturing and supply networks to optimize production planning and control through reinforcement-learning-based agents is also planned. In conclusion, CoJoSim provides a reliable and effective tool for optimizing semiconductor manufacturing and can serve as a benchmark for future implementations. semiconductor manufacturing; complex job shop; reference model; simulation +
Footnote †: journal: Applied Sciences  
located in highly industrialized countries, while the back end is often located in countries with low labor costs due to the higher manual effort involved [4]. In wafer fabrication, the most complex part of the overall process, layers of material with different electrical characteristics are built up on a raw wafer. To build these layers, the following process steps have to be performed repeatedly on the wafer: oxidation/diffusion, film deposition, photolithography, etch, ion implantation, and planarization (cf. Figure 1). For a more in-depth technological description of the process steps, please refer to the literature [4; 10]. The result of this process, which takes place in a dedicated facility referred to as a wafer fab, is a wafer containing between several hundred and several thousand individual devices [5]. Manufacturing in semiconductor wafer fabs, regarding their manufacturing principle referred to as complex job shops, typically contains more than 100 machines and up to 700 process steps resulting in a lead time of up to 3 months [4]. These complex job shops can be characterized by unequal release dates of the jobs, prescribed due dates of the jobs, reentrant flows of the jobs, different types of processes (lot vs. batch), unequal processing times of jobs at one kind of equipment, sequence-dependent setup times and frequent disturbances (e.g., machine breakdowns) [4; 6; 8; 10]. Complex job shops thus differ significantly from job shops [11]. Because of this complexity, the consequences of changes in manufacturing are difficult to predict. They often lead to unexpected behavior as well as under- or over-steering due to the complexity of the manufacturing system [6; 7]. Therefore, simulation has become a proven tool in semiconductor supply network and manufacturing [5; 12]. Although there are reference data sets and reference models for semiconductor manufacturing in complex job shops, they often produce entirely different results in scientific publications and are hardly comparable. Consequently, this leads to the research question of how a reference model could be designed and described in order to enable comparable results.