Welcome to PySCFabSim

A performant & scalable semiconductor fab simulator for scheduling & machine learning research.

Use our event-based simulator for your research and industrial projects.

Discover Install GitHub Repository

how to contribute?

About PySCFabSim

Our tool is an event-based simulator built in pure Python. The was developed for simulating factories up to real-world size, aiming to support the research into new scheduling algorithms from prototyping to large-scale experiments. The simulator comes with a declarative environment definition framework and is out of the box usable with existing reinforcement learning methods, priority-based rules, or evolutionary algorithms.

Our Story

This work was partially funded by KWF project 28472, cms electronics GmbH, FunderMax GmbH, Hirsch Armbänder GmbH, incubed IT GmbH, Infineon Technologies Austria AG, Isovolta AG, Kostwein Holding GmbH, and Privatstiftung Kärntner Sparkasse.

Our Goals

Easy Integration

Join our team

We welcome contributions to our repository, included new plugins and datasets

Bundled Datasets
Simulated Operations / Min
Minutes to Install

Semiconductor Manufacturing Process Model

Our fab model involves many special characteristics and challenges of the semiconductor manufacturing process.

Re-entrant flow

A lot visits a list of machine multiple times.This characteristic of the semicondutory manufacturing makes avoiding congestions difficult.

Batch machines

Some machine can process up to 10 lots of the same operation in parallel.

Breakdowns & maintenances

Machine suffer from regular breakdowns or become unavailable due to planned maintenance.

Rework & inspection steps

Online route changes are possible for faulty lots. Additionally, some inspecition steps are only performed for randomly selected lots.


Some steps enforce machine dedication in the re-entrant flow. Thus, the lot has to visit the same machine multiple times, with several steps in-between.

Sequence-dependent setups

Setup times may depend on both the previous and the required setup.

Our tool is designed to be performant,
for training with high sample-complexity machine learning algorithms! Developed by the Production Systems Group

Our Team

PySCFabSim was developed by the Production Systems group at the Department of Artificial Intelligence and Cybersecurity of the University of Klagenfurt.

Benjamin Kovács

Benjamin Kovács


Benjamin Kovács is a Ph.D. candidate in the Institute for Artificial Intelligence and Cybersecurity at the University of Klagenfurt, Austria. His research topic is the development and application of simulations for optimizing manufacturing processes. He is the main developer and maintainer of this simulator project.

Pierre Tassel

Pierre Tassel


Pierre Tassel is a Ph.D. candidate in the Institute for Artificial Intelligence and Cybersecurity at the University of Klagenfurt, Austria. His current research interests include reinforcement learning methods for combinatorial optimization problems and constraint programming.

Martin Gebser

Martin Gebser


Martin Gebser is professor for Production Systems at the University of Klagenfurt and Graz University of Technology. He received his PhD from the University of Potsdam in 2011, where he worked on theoretical and practical aspects of declarative problem solving methods. He works on applications of modern solving technology addresses, e.g., planning and scheduling, product configuration, and system design.


Source codes, documentation, code examples, installation instructions & more. All in one place.

Recent Appearances & Publication

Our tool has been published in multiple peer-reviewed scientific conferences.
Further conference and journal submissions are in progress.

Self-Supervised & Reinforcement Learning Preprint
Poster presentation at the 2023 34th Advanced Semiconductor Manufacturing Conference  
Reinforcement Learning Environment Presentation at the 2022 Winter Simulation Conference
WSC Paper
Reinforcement Learning Environment Paper at the 2022 Winter Simulation Conference
ASMC Presentation
Presentation at the 2022 33rd Advanced Semiconductor Manufacturing Conference  
ASMC Paper
Paper at the 2022 33rd Advanced Semiconductor Manufacturing Conference  

Our Client Testimonials


We provide three installation options based on the requirements of your project.

  • Pre-built Docker Containers EASY

    free forever
    • Check out the performance HOT
    • Try the Weights & Biases monitoring plugin
    • Generate charts of schedules
    • Play with our examples
    • Reproduce results in papers
    • Use your own datasets
    • No setup outside Docker containers
    • No option to modify the core simulation
    • No option to add custom algorithms
  • PyPI Packages

    free forever
    • Easy installation with pip or conda
    • Interfaces for dispatching strategies, planning tools, and evolutionary algorithms
    • Create (single- or multi-agent) reinforcement learning environments
    • Customize reinforcement learning environments
    • Use your own datasets
    • Develop custom plugins
    • No option to modify the core simulation
  • Latest Source Codes EXPERT

    free forever
    • For experts who look for building upon our tool
    • Fully customizable
    • Extend the simulator
    • Contribute to the codebase
    • Write documentation
    • Join the community
    • Report bugs, feature requests
    • Manual installation of package required

Contact Us

You can contact our team using the e-mail addresses and social accounts, or in person at the University of Klagenfurt in Austria.

Planned conference appearances.

ICAPS 2023
July 8-13, 2023

Charles University
Czech Republic

Demonstration of the simulator's user interface.

July 15-19, 2023

Altis Grand Hotel

Full paper about genetic programming.