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minds.ai attends the 2023 Ray Summit

Update: The recording of the presentation can be found here.

For the third year in a row we are presenting at the Ray Summit! This year our CTO, Jasper van Heugten, will present how we harness the power of Ray to propel the semiconductor industry into a new era of AI-driven innovation. Ray Summit, organized by Anyscale, is the conference for everyone passionate about scalable AI solutions, building LLMs and generative AI applications.

What can you expect from Jasper’s presentation this year? In this year’s presentation we’ll dive deeper into some of the cutting-edge minds.ai Maestro products. All designed to revolutionize  the semiconductor manufacturing industry, specifically how to optimize scheduling in a dynamic fabrication environment. Amongst the examples discussed during the session will be how Ray Reinforcement Learning library (RLlib) is leveraged to reduce the number of setup changes in a schedule. For more details see the abstract below.

Want to delve deeper into the details and chat with Jasper? Don’t hesitate to connect with him on LinkedIn, or better yet, catch him before or after his talk, scheduled for September 19th at 4:00 PM.

For more information about the conference, please  see the official Ray Summit website here.

Hope to see you there!

Semiconductor Fab Production Scheduling Using Deep Reinforcement Learning

Abstract

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.

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