AI at the Edge: Advanced Modeling and Automation for Semiconductor Manufacturing

How Beckhoff and MathWorks unite AI, modeling, and real-time control for the next generation of semi equipment 

In the high-stakes world of semiconductor fabrication, precision isn’t just a virtue, it’s a requirement. From extreme ultraviolet (EUV) lithography to advanced etch systems and metrology tools, every wafer step must be predictable, repeatable, and highly optimized. Yet, despite decades of semi engineering innovation, one of the biggest challenges remains: how to rapidly design, validate, and deploy control and analytics systems that can keep pace with ever-tightening performance requirements and uptime demands. 

Enter Beckhoff and MathWorks with a fusion of technologies that brings together model-based design, simulation, machine learning, and real-time control into the same platform. By seamlessly integrating MATLAB® and Simulink® workflows into Beckhoff’s TwinCAT® 3 automation environment, machine builders, system integrators, and large end users alike can simultaneously shrink development cycles, boost equipment reliability, and harness advanced AI techniques within hard real-time systems. 

From model to machine: The digital transformation

Semiconductor equipment development cycles have traditionally suffered from long iteration loops. Conceptual design, detailed modeling, hardware prototyping, software integration, and control tuning often happen in isolated stages. Mistakes discovered late in this chain of processes lead to cost overruns and delayed product introductions – unacceptable in an industry where every batch counts. 

When leveraging Beckhoff and MathWorks tools, engineers can create a “digital thread” that links early simulations directly to production-ready code. Using Simulink’s graphical modeling language and MATLAB’s algorithm development environment, engineers can: 

• Build accurate digital twins of machines, including motion systems, process loops, and sensors. 

• Implement physics-based simulation and virtual commissioning before a physical build, reducing dependency on early hardware prototypes and time-consuming changes.  

• Design and optimize control strategies, from classical PID loops to model predictive control and adaptive setpoint logic. 

Once validated in simulation, those designs can be automatically translated into TwinCAT 3 runtime objects using targets such as TE1400 (Simulink) and TE1401 (MATLAB). The result? Verified control strategies deployed directly into real-time PLC and motion control systems, eliminating the manual translation and reimplementation steps that have historically introduced bugs and delays into projects.  

Hard real-time meets smart software 

Semiconductor equipment won’t tolerate ambiguity. Control loops run on the order of microseconds, and any AI or analytics functionality can’t compromise deterministic performance. Beckhoff’s TwinCAT 3 runtime, with hard-real-time scheduling and multi-core processor support, ensures that validated models and algorithms execute with consistent timing, even under heavy load.  

At the same time, MATLAB and Simulink bring unmatched algorithmic capabilities: 

• Predictive maintenance and machine health monitoring workflows can be prototyped in

  MATLAB, training state-estimation models using real machine data. 

• Machine learning models for anomaly detection, pattern-based fault prediction, or quality classification can be built, trained, and validated offline. 

• Advanced signal processing and optimization routines can be developed in a high-level environment and then deployed into the TwinCAT runtime where they run alongside traditional control code.  

Crucially for semiconductor fabs, these AI-driven insights can run synchronously with the machine’s control loops, enabling in-control analytics rather than post-process reporting. Predictive algorithms become part of the control decision chain, helping reduce unplanned downtime and improve overall equipment effectiveness (OEE). 

Closed-loop validation: MiL, SiL, HiL 

A key benefit of this Beckhoff and MathWorks ecosystem is the ability to validate designs across multiple stages: 

• Model-in-the-Loop (MiL): Algorithms are tested in isolation against system models before any code generation. 

• Software-in-the-Loop (SiL): The same code that will run on TwinCAT can be evaluated within a virtual environment, checking for logical errors and integration issues. 

• Hardware-in-the-Loop (HiL): Real hardware controllers interact with virtual components in real time, enabling system-level validation before hardware manufacturing.  

This layered validation dramatically lowers risk in all projects, especially expensive ones. For semiconductor machine builders, where control software defects can lead to production line delays and lost revenue, catching bugs early saves both time and millions in avoidable cost. 

Real gains for equipment OEMs and end users 

For machine builders 

Simulation-first workflows reduce engineering effort and enable reuse of standardized control modules across product lines. Teams can: 

• Reduce physical prototyping needs. 

• Accelerate control algorithm development. 

• Leverage MATLAB toolboxes for machine learning, optimization, and statistics without redeveloping from scratch and deploy immediately in TwinCAT. 

For large end user manufacturers 

End user fabs benefit by receiving equipment with more reliable controls, predictive analytics, and comprehensive digital twin capabilities. This translates into: 

• Shorter qualification cycles. 

• Better integration into factory tech stacks. 

• Continuous improvement through offline refinement of deployed models. 

Looking ahead with AI: Go smarter, faster, leaner 

The tight integration of MathWorks technologies with Beckhoff’s automation ecosystem marks a paradigm shift in complex industries like semiconductor manufacturing. It empowers engineers to think in terms of digital design flexibility rather than physical hardware constraints, to embed AI where it matters most, and to move confidently from virtual validation to physical execution in the field. 

As fabs fight against ever-greater complexity and tighter business margins, the ability to close the loop between simulation, AI, and real-time automation isn’t just a competitive advantage, it’s the future of intelligent semiconductor manufacturing. 

Have you decided that it’s time to transform your semi equipment and processes with powerful automation with built-in AI tools for advanced modeling and simulation? Connect with your local Beckhoff sales engineer to get started with TwinCAT and MATLAB®/Simulink® today. 

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Brandon Stiffler is the TwinCAT Product Manager at Beckhoff Automation LLC