Semiconductor Fab Production Scheduling Using Deep Reinforcement Learning

Semiconductor manufacturing is often regarded as the most complex manufacturing process in the world, producing chips in high volume at the nanometer scale. Our work focuses on a tractable method for optimizing scheduling in a dynamic fab. Changing priorities in the fab on a day-to-day basis requires flexible optimization methods to respond to these changes. With our solution fab operators can augment their current workflow to quickly and efficiently create optimized schedules. These schedules are based on a user-defined set of KPIs, e.g. related to cycle times and on-time delivery. Our solution minds.ai Maestro, built on top of RLLib, uses Deep Reinforcement Learning to efficiently interpret and scale to the complex dynamics in the fab in order to arrive at highly optimized schedules, and subsequently bring these into production. minds.ai Maestro runs natively on Linux, macOS and Windows, showing that Windows-only enterprise software can leverage Ray. About Anyscale --- Anyscale is the AI Application Platform for developing, running, and scaling AI. https://www.anyscale.com/ If you're interested in a managed Ray service, check out: https://www.anyscale.com/signup/ About Ray --- Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads. https://docs.ray.io/en/latest/ #llm #machinelearning #ray #deeplearning #distributedsystems #python #genai