Big data analytics is at the brink of changing the landscape in NXP Semiconductors Back End manufacturing operations. Numerous IT tools, implemented over the last decade, collect gigabytes of data daily, though the potential value of this data still remains to be explored. In this paper, the software tool called Heads Up is presented. Heads Up intelligently scans, filters, and explores the data with use of simulation. The software provides real-time relevant information, which is of high value in daily, as well as long term, production management. The software tool has been introduced at the NXP high volume manufacturing plant GuangDong China, where it is about to shift the paradigm on manufacturing operations.

No abstract

This paper addresses batch scheduling at a back-end semiconductor plant of Nexperia. This complex manufacturing environment is characterized by a large product and batch size variety, numerous parallel machines with large capacity differences, sequence and machine dependent setup times and machine eligibility constraints. A hybrid genetic algorithm is proposed to improve the scheduling process, the main features of which are a local search enhanced crossover mechanism, two additional fast local search procedures and a user-controlled multi-objective fitness function. Testing with real-life production data shows that this multi-objective approach can strike the desired balance between production time, setup time and tardiness, yielding high-quality practically feasible production schedules.